Initial mcwaddams documentation site

Astro + Starlight documentation site for mcwaddams MCP server.

Features:
- Diátaxis documentation structure (tutorials, how-to, reference, explanation)
- Office Space theming (Milton Waddams, TPS Reports, red Swingline)
- 29 documentation pages covering all 20 tools
- TPS Reports section for test results
- Flair gamification config (pieces of documentation flair)
- Custom CSS with Office Space color scheme

Structure:
- Getting Started: backstory, installation, quickstart
- Tutorials: first extraction, legacy formats, indexing, resources
- How-To: tables, Excel analysis, markdown, pagination, URLs
- Reference: all tools, universal/word/excel tools, MCP resources, formats
- Explanation: architecture, mixins, fallbacks, resource system
- TPS Reports: dashboard, coverage, torture tests
- Community: credits, feedback, leaderboard
This commit is contained in:
Ryan Malloy 2026-01-11 12:21:49 -07:00
commit 32b41f79d9
42 changed files with 10513 additions and 0 deletions

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# build output
dist/
# generated types
.astro/
# dependencies
node_modules/
# logs
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
# environment variables
.env
.env.production
# macOS-specific files
.DS_Store

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# Starlight Starter Kit: Basics
[![Built with Starlight](https://astro.badg.es/v2/built-with-starlight/tiny.svg)](https://starlight.astro.build)
```
npm create astro@latest -- --template starlight
```
> 🧑‍🚀 **Seasoned astronaut?** Delete this file. Have fun!
## 🚀 Project Structure
Inside of your Astro + Starlight project, you'll see the following folders and files:
```
.
├── public/
├── src/
│ ├── assets/
│ ├── content/
│ │ └── docs/
│ └── content.config.ts
├── astro.config.mjs
├── package.json
└── tsconfig.json
```
Starlight looks for `.md` or `.mdx` files in the `src/content/docs/` directory. Each file is exposed as a route based on its file name.
Images can be added to `src/assets/` and embedded in Markdown with a relative link.
Static assets, like favicons, can be placed in the `public/` directory.
## 🧞 Commands
All commands are run from the root of the project, from a terminal:
| Command | Action |
| :------------------------ | :----------------------------------------------- |
| `npm install` | Installs dependencies |
| `npm run dev` | Starts local dev server at `localhost:4321` |
| `npm run build` | Build your production site to `./dist/` |
| `npm run preview` | Preview your build locally, before deploying |
| `npm run astro ...` | Run CLI commands like `astro add`, `astro check` |
| `npm run astro -- --help` | Get help using the Astro CLI |
## 👀 Want to learn more?
Check out [Starlights docs](https://starlight.astro.build/), read [the Astro documentation](https://docs.astro.build), or jump into the [Astro Discord server](https://astro.build/chat).

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// @ts-check
import { defineConfig } from 'astro/config';
import starlight from '@astrojs/starlight';
import tailwindcss from '@tailwindcss/vite';
// https://astro.build/config
export default defineConfig({
vite: {
plugins: [tailwindcss()],
},
integrations: [
starlight({
title: 'mcwaddams',
tagline: 'I was told there would be document extraction.',
logo: {
src: './src/assets/stapler.svg',
replacesTitle: false,
},
social: [
{ icon: 'github', label: 'GitHub', href: 'https://github.com/ryanmalloy/mcwaddams' },
],
customCss: [
'./src/styles/custom.css',
],
head: [
{
tag: 'meta',
attrs: {
name: 'theme-color',
content: '#b91c1c',
},
},
],
sidebar: [
{
label: 'Getting Started',
items: [
{ label: 'The Backstory', slug: 'backstory' },
{ label: 'Installation', slug: 'installation' },
{ label: 'Quick Start', slug: 'quickstart' },
],
},
{
label: 'Tutorials',
badge: { text: 'Learn', variant: 'tip' },
items: [
{ label: 'Your First Extraction', slug: 'tutorials/first-extraction' },
{ label: 'Working with Legacy Formats', slug: 'tutorials/legacy-formats' },
{ label: 'Indexing Large Documents', slug: 'tutorials/indexing' },
{ label: 'Using MCP Resources', slug: 'tutorials/resources' },
],
},
{
label: 'How-To Guides',
badge: { text: 'Solve', variant: 'note' },
items: [
{ label: 'Extract Tables from Word', slug: 'how-to/extract-tables' },
{ label: 'Analyze Excel Data', slug: 'how-to/analyze-excel' },
{ label: 'Convert to Markdown', slug: 'how-to/convert-markdown' },
{ label: 'Handle Pagination', slug: 'how-to/pagination' },
{ label: 'Process URLs', slug: 'how-to/url-processing' },
],
},
{
label: 'Reference',
badge: { text: 'Look Up', variant: 'caution' },
items: [
{ label: 'All Tools', slug: 'reference/tools' },
{ label: 'Universal Tools', slug: 'reference/universal-tools' },
{ label: 'Word Tools', slug: 'reference/word-tools' },
{ label: 'Excel Tools', slug: 'reference/excel-tools' },
{ label: 'MCP Resources', slug: 'reference/resources' },
{ label: 'Format Support', slug: 'reference/formats' },
],
},
{
label: 'Explanation',
badge: { text: 'Understand', variant: 'success' },
items: [
{ label: 'Architecture', slug: 'explanation/architecture' },
{ label: 'Why Mixins?', slug: 'explanation/mixins' },
{ label: 'Fallback Strategy', slug: 'explanation/fallbacks' },
{ label: 'Resource System', slug: 'explanation/resource-system' },
],
},
{
label: 'TPS Reports',
badge: { text: 'Testing Painful Stuff', variant: 'danger' },
items: [
{ label: 'Test Dashboard', slug: 'tps/dashboard' },
{ label: 'Coverage Report', slug: 'tps/coverage' },
{ label: 'Torture Tests', slug: 'tps/torture' },
],
},
{
label: 'Community',
items: [
{ label: 'Feedback', slug: 'community/feedback' },
{ label: 'Flair Leaderboard', slug: 'community/leaderboard' },
{ label: 'Credits', slug: 'community/credits' },
],
},
],
editLink: {
baseUrl: 'https://github.com/ryanmalloy/mcwaddams-site/edit/main/',
},
lastUpdated: true,
pagination: true,
tableOfContents: { minHeadingLevel: 2, maxHeadingLevel: 4 },
expressiveCode: {
themes: ['dracula', 'github-light'],
styleOverrides: {
borderRadius: '0.5rem',
},
},
}),
],
telemetry: false,
devToolbar: { enabled: false },
});

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{
"name": "mcwaddams-site",
"type": "module",
"version": "0.0.1",
"scripts": {
"dev": "astro dev",
"start": "astro dev",
"build": "astro build",
"preview": "astro preview",
"astro": "astro"
},
"dependencies": {
"@astrojs/starlight": "^0.37.2",
"@tailwindcss/vite": "^4.1.18",
"astro": "^5.6.1",
"sharp": "^0.34.2",
"tailwindcss": "^4.1.18"
}
}

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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 128 128"><path fill-rule="evenodd" d="M81 36 64 0 47 36l-1 2-9-10a6 6 0 0 0-9 9l10 10h-2L0 64l36 17h2L28 91a6 6 0 1 0 9 9l9-10 1 2 17 36 17-36v-2l9 10a6 6 0 1 0 9-9l-9-9 2-1 36-17-36-17-2-1 9-9a6 6 0 1 0-9-9l-9 10v-2Zm-17 2-2 5c-4 8-11 15-19 19l-5 2 5 2c8 4 15 11 19 19l2 5 2-5c4-8 11-15 19-19l5-2-5-2c-8-4-15-11-19-19l-2-5Z" clip-rule="evenodd"/><path d="M118 19a6 6 0 0 0-9-9l-3 3a6 6 0 1 0 9 9l3-3Zm-96 4c-2 2-6 2-9 0l-3-3a6 6 0 1 1 9-9l3 3c3 2 3 6 0 9Zm0 82c-2-2-6-2-9 0l-3 3a6 6 0 1 0 9 9l3-3c3-2 3-6 0-9Zm96 4a6 6 0 0 1-9 9l-3-3a6 6 0 1 1 9-9l3 3Z"/><style>path{fill:#000}@media (prefers-color-scheme:dark){path{fill:#fff}}</style></svg>

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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" width="32" height="32">
<!-- Red Swingline Stapler -->
<defs>
<linearGradient id="staplerRed" x1="0%" y1="0%" x2="0%" y2="100%">
<stop offset="0%" style="stop-color:#ef4444"/>
<stop offset="50%" style="stop-color:#dc2626"/>
<stop offset="100%" style="stop-color:#b91c1c"/>
</linearGradient>
<linearGradient id="staplerDark" x1="0%" y1="0%" x2="0%" y2="100%">
<stop offset="0%" style="stop-color:#374151"/>
<stop offset="100%" style="stop-color:#1f2937"/>
</linearGradient>
</defs>
<!-- Base/Bottom -->
<rect x="8" y="42" width="48" height="8" rx="2" fill="url(#staplerDark)"/>
<!-- Staple channel -->
<rect x="12" y="44" width="40" height="4" rx="1" fill="#111827"/>
<!-- Top/Body -->
<path d="M10 42 L10 28 Q10 24 14 24 L50 24 Q54 24 54 28 L54 42 Z" fill="url(#staplerRed)"/>
<!-- Highlight -->
<path d="M12 26 L52 26 Q53 26 53 27 L53 30 L11 30 L11 27 Q11 26 12 26 Z" fill="rgba(255,255,255,0.2)"/>
<!-- Swingline logo area -->
<rect x="20" y="30" width="24" height="8" rx="1" fill="rgba(0,0,0,0.1)"/>
<!-- Push lever -->
<rect x="44" y="20" width="8" height="6" rx="1" fill="url(#staplerDark)"/>
</svg>

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import { defineCollection } from 'astro:content';
import { docsLoader } from '@astrojs/starlight/loaders';
import { docsSchema } from '@astrojs/starlight/schema';
export const collections = {
docs: defineCollection({ loader: docsLoader(), schema: docsSchema() }),
};

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---
title: The Backstory
description: How Milton Waddams became the patron saint of legacy document processing.
---
import { Aside } from '@astrojs/starlight/components';
# The Backstory
> *"I was told I could listen to the radio at a reasonable volume from nine to eleven..."*
## The Relocation
Milton Waddams was relocated to the basement. They took his stapler. But down there, surrounded by boxes of `.doc` files from 1997 and `.xls` spreadsheets that predate Unicode, he became something else entirely.
He became a **document processing expert**.
<Aside type="tip" title="Fun Fact">
After the movie *Office Space* came out, Swingline didn't actually make a red stapler. Demand was so high they started manufacturing them. The movie created the product.
</Aside>
## The Problem
Every enterprise has them:
- **The Archive Folder** — 50,000 Word documents from before the cloud existed
- **The Legacy Database Export** — Excel files with formulas referencing cells that no longer exist
- **The Board Presentations** — PowerPoint decks with embedded charts from 2003
- **The Contract Repository** — `.doc` files that crash modern Word
AI agents can read PDFs. They can parse JSON. But Office documents? The binary formats, the OLE containers, the OOXML with custom schemas?
*Nobody wants to deal with that.*
## The Solution
**mcwaddams** handles the documents nobody else wants to touch.
```python
# Extract text from a 1997 .doc file
result = await extract_text("contract_final_FINAL_v2.doc")
# It just works
print(result["text"])
```
### What We Handle
| Format | Era | Status |
|--------|-----|--------|
| `.docx` | 2007+ | ✅ Full support |
| `.doc` | 1997-2007 | ✅ Works fine |
| `.xlsx` | 2007+ | ✅ Full support |
| `.xls` | 1997-2007 | ✅ Works fine |
| `.pptx` | 2007+ | ✅ Full support |
| `.ppt` | 1997-2007 | ✅ Works fine |
<Aside type="caution" title="Did You Get the Memo?">
If a document is password-protected or encrypted, we'll detect it and tell you. We can't extract content from encrypted files, but we won't silently fail either.
</Aside>
## The Philosophy
### 1. No Silent Failures
When python-docx can't handle a file, mammoth tries. When openpyxl fails, pandas steps in. You'll always get either content or a clear error message explaining why.
### 2. Legacy is Not Abandoned
Those `.doc` files from 2003? They're still business-critical for someone. We don't treat legacy formats as second-class citizens.
### 3. Context-Aware Extraction
Large documents get paginated automatically. The MCP resource system lets you fetch chapters on-demand. Your context window stays manageable.
### 4. Testing Painful Stuff
We threw 301 random Office documents at mcwaddams. **299 succeeded.** The 2 failures were empty/corrupt files.
See the [TPS Reports](/tps/dashboard/) for proof.
---
## The Name
Milton Waddams. The guy with the stapler. Relegated to the basement with the old filing cabinets and the roaches.
That's where the legacy documents live too.
*"I could set the building on fire..."*
---
<div style="text-align: center; margin-top: 2rem;">
**Ready to start?** → [Installation](/installation/)
</div>

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---
title: Credits & Attributions
description: The open source projects and cultural references that make mcwaddams possible.
---
import { Aside, Card, CardGrid, LinkCard } from '@astrojs/starlight/components';
# Credits & Attributions
> *"It's not just about me and my dream of doing nothing."*
mcwaddams stands on the shoulders of giants — both technical and cinematic.
---
## Open Source Dependencies
The tools that make document extraction possible.
<CardGrid>
<Card title="python-docx" icon="document">
Modern Word document processing. The workhorse of `.docx` extraction.
<br/><small>MIT License</small>
</Card>
<Card title="openpyxl" icon="document">
Excel XLSX file processing with full formula support.
<br/><small>MIT License</small>
</Card>
<Card title="python-pptx" icon="document">
PowerPoint PPTX processing for slides and speaker notes.
<br/><small>MIT License</small>
</Card>
<Card title="mammoth" icon="document">
Enhanced Word to HTML/Markdown conversion. Our fallback hero.
<br/><small>BSD-2-Clause</small>
</Card>
</CardGrid>
<CardGrid>
<Card title="pandas" icon="document">
Data analysis powerhouse. Handles CSV and Excel fallbacks.
<br/><small>BSD-3-Clause</small>
</Card>
<Card title="olefile" icon="document">
OLE Compound Document parsing for legacy `.doc`, `.xls`, `.ppt`.
<br/><small>BSD-2-Clause</small>
</Card>
<Card title="xlrd" icon="document">
Legacy Excel XLS support. Because 2003 never really left.
<br/><small>BSD License</small>
</Card>
<Card title="Pillow" icon="document">
Image processing for embedded graphics extraction.
<br/><small>HPND License</small>
</Card>
</CardGrid>
### Framework & Tools
| Project | Purpose | License |
|---------|---------|---------|
| [FastMCP](https://github.com/jlowin/fastmcp) | MCP server framework | MIT |
| [Astro](https://astro.build) | This documentation site | MIT |
| [Starlight](https://starlight.astro.build) | Documentation theme | MIT |
| [Tailwind CSS](https://tailwindcss.com) | Styling | MIT |
---
## Office Space
The cultural foundation of this project.
<Aside type="tip" title="Why Office Space?">
Mike Judge's 1999 film isn't just a comedy — it's a documentary about corporate dysfunction that resonates with anyone who's ever debugged a printer or processed legacy documents.
</Aside>
### The References
| Reference | Location | Why It Fits |
|-----------|----------|-------------|
| **Milton Waddams** | Project name | Relegated to the basement with legacy documents |
| **TPS Reports** | Test section | "Testing Painful Stuff" — `.doc` from 1997 is painful |
| **Red Swingline** | Logo/branding | The small things that matter |
| **"Did you get the memo?"** | Throughout | Clear documentation is essential |
| **"I could set the building on fire"** | Footer | But we'd rather process documents |
| **Pieces of Flair** | Coming soon | Gamification for the bureaucratic soul |
### Key Quotes Used
> *"I was told there would be document extraction."*
Adaptation of Milton's "I was told there would be cake" — except here, there actually is document extraction.
> *"Did you get the memo about the TPS reports?"*
Our test section is literally called TPS Reports. Because testing legacy formats is painful, and we test it so you don't have to.
> *"I believe you have my stapler..."*
The persistence of caring about the small things. Like proper Unicode handling in a `.doc` from 1997.
---
## Author
<Card title="Ryan Malloy" icon="external">
Built by Ryan Malloy at [Supported Systems](https://supported.systems).
<br/><br/>
- [ryanmalloy.com](https://ryanmalloy.com) — Personal site
- [GitHub](https://github.com/ryanmalloy) — Open source work
<br/><br/>
<small>
Read: [AI Discernment vs AI Criticism](https://ryanmalloy.com/collaborations/ai-discernment-vs-criticism/) —
On building with AI while maintaining craft standards.
</small>
</Card>
---
## Special Thanks
### The Python Community
For building and maintaining the libraries that make Office document processing possible. Every commit to `python-docx` or `openpyxl` makes legacy document handling slightly less painful.
### The MCP Ecosystem
For creating a protocol that lets AI agents access tools without reinventing the wheel. mcwaddams exists because FastMCP made it trivial to build.
### Mike Judge
For creating Office Space and giving the tech industry a shared vocabulary for describing workplace dysfunction. The red stapler is a symbol of what happens when you ignore the people doing the actual work.
### Swingline
For actually manufacturing a red stapler after the movie came out. Sometimes life imitates art. (Fun fact: the prop department painted a black stapler red because Swingline didn't make that color in 1999.)
---
## License
mcwaddams is released under the MIT License.
```
MIT License
Copyright (c) 2024 Ryan Malloy
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
```
---
<div style="text-align: center; margin-top: 3rem;">
🔴
*I believe you have my documentation...*
<small>Unlike Milton's stapler, this one's MIT licensed. Take it.</small>
</div>

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---
title: Feedback
description: Found a bug? Have an idea? We want to hear it.
---
import { Aside, Card, CardGrid, LinkCard } from '@astrojs/starlight/components';
# Feedback
> *"Excuse me, I believe you have my bug report..."*
We want to hear from you — bugs, feature requests, or just letting us know what works.
## Report Issues
<LinkCard
title="GitHub Issues"
description="Report bugs or request features on our GitHub repository."
href="https://github.com/ryanmalloy/mcwaddams/issues"
/>
## What Makes a Good Bug Report
1. **Document format** — What type of file was it?
2. **What you tried** — The exact tool/prompt used
3. **What happened** — The actual result
4. **What you expected** — The desired result
5. **Sample file** — If possible (anonymized)
<Aside type="tip" title="Sensitive Documents">
If your document contains sensitive data, describe the structure without including the actual content. Or create a minimal reproduction file.
</Aside>
## Feature Requests
Have an idea for a new tool or improvement? Open an issue with the `enhancement` label.
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
*"I could set the building on fire..."*
<br/>
<small>Please don't. Just open an issue.</small>
</div>

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---
title: Flair Leaderboard
description: Who's got the most pieces of documentation flair?
---
import { Aside } from '@astrojs/starlight/components';
# Flair Leaderboard
> *"You know, the Nazis had pieces of flair they made the Jews wear."*
> *"...okay, we're going to need to talk about your TPS reports."*
## Collect Flair
Spend time on documentation pages to earn pieces of flair. Each page has its own badge:
| Page | Flair |
|------|-------|
| Home | 📄 "I Was Told There Would Be Extraction" |
| Backstory | 🔴 "Basement Dweller" |
| Installation | 🖨️ "PC Load Letter" |
| Quick Start | ☕ "Case of the Mondays" |
| Tools Reference | 📋 "TPS Report Expert" |
| Architecture | 👔 "The Bobs Approved" |
| Test Dashboard | 📝 "Did You Get The Memo?" |
| Torture Tests | 😮 "O Face" |
| Credits | 🔴 "I Have Your Stapler" |
<Aside type="caution" title="Coming Soon">
Full gamification system with localStorage persistence and optional leaderboard API integration.
</Aside>
## The Goal
Collect all 12 pieces of flair. Brian from accounting has 37, but we only ask for the minimum.
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
*"We need to talk about your flair..."*
</div>

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---
title: Architecture
description: How mcwaddams processes Office documents behind the scenes.
---
import { Aside } from '@astrojs/starlight/components';
# Architecture
> *"So I was sitting in my cubicle today, and I realized... every document format is worse than the one before it."*
mcwaddams is designed around a single principle: **never silently fail**. When extraction works, you get content. When it doesn't, you get a clear explanation why.
---
## High-Level Overview
```
┌─────────────────────────────────────────────────────────────┐
│ MCP Client │
│ (Claude Code, Claude Desktop) │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ FastMCP Server │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Universal │ │ Word │ │ Excel │ │
│ │ Mixin │ │ Mixin │ │ Mixin │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │ │ │ │
│ └────────────────┼────────────────┘ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Format Detection & Routing │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────┼────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌───────────┐ ┌───────────┐ ┌───────────┐ │
│ │ python- │ │ openpyxl │ │ python- │ │
│ │ docx │ │ pandas │ │ pptx │ │
│ └───────────┘ └───────────┘ └───────────┘ │
│ │ │ │ │
│ └────────────────┼────────────────┘ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Fallback Handlers │ │
│ │ mammoth · olefile · xlrd · legacy parsers │ │
│ └─────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
```
---
## The Mixin Pattern
mcwaddams uses **mixins** to organize tools by document type:
### Why Mixins?
```python
class MCWaddamsServer(
UniversalMixin, # extract_text, extract_images, etc.
WordMixin, # convert_to_markdown, extract_tables, etc.
ExcelMixin, # analyze_data, extract_formulas, etc.
):
"""The main server combines all capabilities."""
pass
```
**Benefits:**
1. **Separation of concerns** — Word tools don't clutter Excel code
2. **Independent testing** — Each mixin can be tested in isolation
3. **Easy extension** — Add PowerPoint tools without touching existing code
4. **Clear ownership** — Easy to find where a tool is defined
<Aside type="note" title="Why Not Separate Servers?">
One server with mixins beats multiple MCP servers because:
- Single point of configuration
- Shared utilities (validation, caching)
- Cross-format operations (compare Word to Excel)
- Simpler user experience
</Aside>
---
## Format Detection
Every request starts with format detection:
```python
async def detect_format(file_path: str) -> FormatInfo:
# 1. Check file extension
ext = Path(file_path).suffix.lower()
# 2. Validate file exists and is readable
validate_file(file_path)
# 3. Detect actual format (extension might lie)
with open(file_path, 'rb') as f:
magic_bytes = f.read(8)
# 4. Check for encryption
is_encrypted = check_encryption(file_path)
return FormatInfo(
extension=ext,
actual_format=detect_from_magic(magic_bytes),
is_legacy=(ext in ['.doc', '.xls', '.ppt']),
is_encrypted=is_encrypted
)
```
### Magic Bytes
We don't trust file extensions alone:
| Magic Bytes | Format |
|-------------|--------|
| `50 4B 03 04` | ZIP-based (OOXML: docx/xlsx/pptx) |
| `D0 CF 11 E0` | OLE Compound (doc/xls/ppt) |
| `EF BB BF` or starts with text | CSV/plain text |
---
## The Fallback Strategy
Primary methods are tried first, then fallbacks:
```python
async def extract_text(file_path: str, method: str = "auto"):
if method == "auto":
# Try primary method
try:
return await primary_extraction(file_path)
except ExtractionError:
pass
# Try fallback
try:
return await fallback_extraction(file_path)
except ExtractionError:
pass
# All methods failed — return helpful error
return {
"error": "Extraction failed",
"tried": ["python-docx", "mammoth"],
"hint": "File may be corrupted. Try analyze_document_health."
}
```
### Fallback Chain
| Format | Primary | Fallback 1 | Fallback 2 |
|--------|---------|------------|------------|
| `.docx` | python-docx | mammoth | — |
| `.doc` | olefile | textract* | raw OLE |
| `.xlsx` | openpyxl | pandas | — |
| `.xls` | xlrd | pandas | olefile |
| `.pptx` | python-pptx | — | — |
| `.csv` | pandas | built-in csv | — |
<small>*textract only if installed</small>
---
## Resource System
For large documents, the resource system enables on-demand fetching:
```
┌─────────────────────────────────────────────────────────────┐
│ index_document() │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Resource Store │
│ │
│ doc_id: "abc123def456" │
│ │
│ chapters: [ │
│ {id: 1, uri: "chapter://abc123/1", content: "..."}, │
│ {id: 2, uri: "chapter://abc123/2", content: "..."}, │
│ ] │
│ │
│ images: [ │
│ {id: 0, uri: "image://abc123/0", data: <bytes>}, │
│ ] │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ MCP Resource Protocol │
│ │
│ Client requests: chapter://abc123/1.md │
│ Server returns: Markdown-formatted Chapter 1 │
└─────────────────────────────────────────────────────────────┘
```
### URI Schemes
- `chapter://doc_id/N` — Single chapter
- `chapter://doc_id/N.md` — Chapter as Markdown
- `chapter://doc_id/N.txt` — Chapter as plain text
- `chapter://doc_id/N.html` — Chapter as HTML
- `chapters://doc_id/N-M` — Range of chapters
- `image://doc_id/N` — Single image
- `sheet://doc_id/name` — Excel sheet
- `slide://doc_id/N` — PowerPoint slide
---
## URL Processing & Caching
Documents from URLs are downloaded and cached:
```python
CACHE_DURATION = 3600 # 1 hour
async def resolve_file_path(path_or_url: str) -> str:
if not path_or_url.startswith(('http://', 'https://')):
return path_or_url # Local file
# Check cache
cache_key = hash_url(path_or_url)
cached = get_from_cache(cache_key)
if cached and not expired(cached):
return cached.path
# Download
async with aiohttp.ClientSession() as session:
async with session.get(path_or_url) as response:
content = await response.read()
# Save to temp and cache
temp_path = save_to_temp(content, extension_from_url(path_or_url))
add_to_cache(cache_key, temp_path)
return temp_path
```
---
## Error Handling Philosophy
Every error must be:
1. **Clear** — What went wrong
2. **Actionable** — What can be done about it
3. **Helpful** — Suggestions for next steps
```python
class ExtractionError(Exception):
def __init__(self, message: str, hint: str = None, tried: list = None):
self.message = message
self.hint = hint or "Check file integrity with analyze_document_health"
self.tried = tried or []
def to_dict(self):
return {
"error": self.message,
"hint": self.hint,
"tried_methods": self.tried
}
```
---
## Performance Considerations
### Token Limits
Large documents are paginated at ~25,000 tokens:
```python
MAX_TOKENS = 25000
TOKENS_PER_CHAR = 0.25 # Rough estimate
def should_paginate(content: str) -> bool:
estimated_tokens = len(content) * TOKENS_PER_CHAR
return estimated_tokens > MAX_TOKENS
```
### Lazy Loading
Resources aren't loaded until requested:
```python
# index_document only creates references
index = await index_document("huge-book.docx")
# Returns immediately with URIs
# Content loaded only when requested
chapter_1 = await mcp_resources.read("chapter://abc123/1")
# Now the content is loaded
```
---
## Extending mcwaddams
Want to add new functionality?
### Add a New Tool
```python
# In the appropriate mixin
@mcp.tool()
async def my_new_tool(file_path: str) -> dict:
"""Description for MCP discovery."""
path = await resolve_file_path(file_path)
validate_file(path)
# Your logic here
return {"result": "..."}
```
### Add Format Support
```python
# In format detection
FORMAT_HANDLERS = {
'.docx': DocxHandler,
'.doc': DocHandler,
'.rtf': RtfHandler, # Add new format
}
```
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
"What would you say... you do here?"
<br/>
<small>We extract documents. It's pretty straightforward.</small>
</div>

View File

@ -0,0 +1,31 @@
---
title: Fallback Strategy
description: How mcwaddams tries multiple methods to extract your documents.
---
import { Aside } from '@astrojs/starlight/components';
# Fallback Strategy
> *"We fixed the glitch."*
When the primary extraction method fails, mcwaddams automatically tries alternatives.
## Fallback Chain
| Format | Primary | Fallback |
|--------|---------|----------|
| `.docx` | python-docx | mammoth |
| `.doc` | olefile | textract |
| `.xlsx` | openpyxl | pandas |
| `.xls` | xlrd | pandas |
## The Philosophy
1. **Try the best method first** — Usually works
2. **Fall back gracefully** — Alternative parsers step in
3. **Never silently fail** — Always return content or a clear error
<Aside type="note">
See [Architecture](/explanation/architecture/) for implementation details.
</Aside>

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@ -0,0 +1,35 @@
---
title: Why Mixins?
description: The architectural decision behind mcwaddams's modular structure.
---
import { Aside } from '@astrojs/starlight/components';
# Why Mixins?
> *"What would you say... you do here?"*
mcwaddams uses Python mixins to organize 20 tools into logical groups without creating multiple MCP servers.
## The Pattern
```python
class MCWaddamsServer(
UniversalMixin, # 7 cross-format tools
WordMixin, # 10 Word-specific tools
ExcelMixin, # 3 Excel-specific tools
):
pass
```
## Benefits
1. **Single server** — One MCP configuration, not three
2. **Shared utilities** — Validation, caching, format detection
3. **Clear boundaries** — Each mixin owns its tools
4. **Easy testing** — Test mixins in isolation
5. **Simple extension** — Add PowerPoint mixin without touching Word code
<Aside type="note">
See [Architecture](/explanation/architecture/) for the full design.
</Aside>

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@ -0,0 +1,35 @@
---
title: Resource System
description: How MCP resources enable on-demand document access.
---
import { Aside } from '@astrojs/starlight/components';
# Resource System
> *"I was told I could fetch chapters at a reasonable rate..."*
The MCP resource system lets you access parts of indexed documents without reprocessing.
## How It Works
```
index_document("huge-book.docx")
→ Creates resource references
→ Stores content in memory
→ Returns URIs for on-demand access
mcp_resources.read("chapter://abc123/1")
→ Fetches only Chapter 1
→ No re-parsing the document
```
## Why Resources?
- **Context window management** — Don't load 500 pages at once
- **Selective access** — Fetch only what you need
- **Format conversion** — `.md`, `.txt`, `.html` on the fly
<Aside type="note">
See [MCP Resources Reference](/reference/resources/) for URI patterns.
</Aside>

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@ -0,0 +1,31 @@
---
title: Analyze Excel Data
description: Get statistical analysis and data quality insights from spreadsheets.
---
import { Aside } from '@astrojs/starlight/components';
# Analyze Excel Data
> *"I did nothing and it was everything I thought it could be."*
Get comprehensive insights from Excel spreadsheets including statistics, data types, and quality checks.
```python
result = await analyze_excel_data(
file_path="sales-data.xlsx",
include_statistics=True,
check_data_quality=True
)
```
**Returns:**
- Column data types
- Statistical summaries (mean, median, std dev)
- Missing value detection
- Duplicate identification
- Outlier flagging
<Aside type="caution" title="Coming Soon">
Detailed analysis guide in progress.
</Aside>

View File

@ -0,0 +1,26 @@
---
title: Convert to Markdown
description: Transform Word documents into clean Markdown.
---
import { Aside } from '@astrojs/starlight/components';
# Convert to Markdown
> *"Why should I change? The document format's the one who sucks."*
Convert Word documents to Markdown while preserving structure, headings, lists, and tables.
```python
result = await convert_to_markdown(
file_path="report.docx",
preserve_structure=True,
include_images=True
)
```
Images are extracted to files and linked in the Markdown output.
<Aside type="caution" title="Coming Soon">
Detailed conversion guide in progress.
</Aside>

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@ -0,0 +1,256 @@
---
title: Extract Tables from Word Documents
description: Get structured table data from Word documents in multiple formats.
---
import { Aside, Steps, Tabs, TabItem } from '@astrojs/starlight/components';
# Extract Tables from Word Documents
> *"We need to talk about your table extraction..."*
Word documents often contain important data locked in tables. mcwaddams extracts them as structured data you can actually use.
---
## The Problem
You have a Word document with a table like this:
| Product | Q1 | Q2 | Q3 | Q4 |
|---------|-----|-----|-----|-----|
| Widget A | 1,234 | 1,456 | 1,678 | 1,890 |
| Widget B | 987 | 1,012 | 1,045 | 1,089 |
| Widget C | 456 | 489 | 512 | 534 |
You need that data in a format you can process, not trapped in a binary `.docx` file.
---
## The Solution
```
Extract tables from quarterly-report.docx as markdown
```
**Result:**
```markdown
| Product | Q1 | Q2 | Q3 | Q4 |
|---------|-----|-----|-----|-----|
| Widget A | 1,234 | 1,456 | 1,678 | 1,890 |
| Widget B | 987 | 1,012 | 1,045 | 1,089 |
| Widget C | 456 | 489 | 512 | 534 |
```
---
## Output Formats
<Tabs>
<TabItem label="Markdown">
```
Extract tables from report.docx as markdown
```
Perfect for documentation, README files, or pasting into other documents.
</TabItem>
<TabItem label="JSON">
```
Extract tables from report.docx as JSON
```
Returns structured data:
```json
{
"tables": [
{
"index": 0,
"headers": ["Product", "Q1", "Q2", "Q3", "Q4"],
"rows": [
["Widget A", "1,234", "1,456", "1,678", "1,890"],
["Widget B", "987", "1,012", "1,045", "1,089"]
]
}
]
}
```
</TabItem>
<TabItem label="CSV">
```
Extract tables from report.docx as CSV
```
Ready for spreadsheet import:
```
Product,Q1,Q2,Q3,Q4
Widget A,1234,1456,1678,1890
Widget B,987,1012,1045,1089
```
</TabItem>
<TabItem label="Structured">
```
Extract tables from report.docx
```
Full metadata with styling information:
```json
{
"tables": [
{
"index": 0,
"position": "after paragraph 12",
"headers": ["Product", "Q1", "Q2", "Q3", "Q4"],
"rows": [...],
"styling": {
"has_header_row": true,
"borders": "all",
"alignment": "left"
}
}
]
}
```
</TabItem>
</Tabs>
---
## Handling Complex Tables
### Merged Cells
Tables with merged cells are normalized:
```
Extract tables, preserve merged cells
```
The tool maintains cell relationships while making the data usable.
### Nested Tables
Tables within tables? We handle those too:
```json
{
"tables": [
{
"index": 0,
"nested_tables": [
{
"cell": [1, 2],
"data": [...]
}
]
}
]
}
```
### Large Tables
Tables spanning multiple pages extract completely — no truncation.
---
## Multiple Tables
Documents with several tables return all of them:
```
Extract all tables from multi-section-report.docx
```
```json
{
"tables": [
{"index": 0, "context": "Executive Summary", "rows": 5},
{"index": 1, "context": "Financial Data", "rows": 24},
{"index": 2, "context": "Appendix A", "rows": 100}
],
"total_tables": 3
}
```
---
## Specific Table Selection
Extract only the table you need:
```
Extract the second table from report.docx
```
Or by context:
```
Extract the table under "Financial Summary"
```
---
## Common Issues
### Empty Cells
Empty cells are preserved as empty strings, not null:
```json
{
"rows": [
["Widget A", "", "1,456", "1,678", "1,890"]
]
}
```
### Formatting in Cells
Bold, italic, and other formatting is stripped for clean data. If you need styled output, use markdown format.
### Numeric Detection
Numbers remain as strings to preserve formatting (commas, currency symbols). Convert as needed:
```python
import json
data = json.loads(result)
# Convert strings to numbers
for row in data["tables"][0]["rows"]:
row[1] = int(row[1].replace(",", ""))
```
---
## Legacy Documents
Tables in `.doc` files work the same way:
```
Extract tables from old-report.doc as JSON
```
The OLE parser handles legacy table structures automatically.
<Aside type="note">
Legacy `.doc` tables may have slight formatting differences, but data extraction is reliable.
</Aside>
---
## Next Steps
- **[Analyze Excel Data](/how-to/analyze-excel/)** — Statistical analysis of spreadsheets
- **[Convert to Markdown](/how-to/convert-markdown/)** — Full document conversion
- **[All Tools Reference](/reference/tools/)** — Complete documentation
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
"PC Load Letter? What does that mean?"
<br/>
<small>We don't know either, but at least tables make sense now.</small>
</div>

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@ -0,0 +1,32 @@
---
title: Handle Pagination
description: Work with documents that exceed token limits.
---
import { Aside } from '@astrojs/starlight/components';
# Handle Pagination
> *"Yeah, I'm gonna need you to go ahead and come in on Saturday for page 2..."*
Documents over 25,000 tokens are automatically paginated. Use cursors to fetch subsequent pages.
```json
{
"text": "Chapter 1...",
"pagination": {
"current_page": 1,
"total_pages": 5,
"cursor_id": "abc123"
}
}
```
Continue with:
```
Continue extraction with cursor abc123
```
<Aside type="caution" title="Coming Soon">
Detailed pagination guide in progress.
</Aside>

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@ -0,0 +1,24 @@
---
title: Process URLs
description: Extract documents directly from HTTP/HTTPS URLs.
---
import { Aside } from '@astrojs/starlight/components';
# Process URLs
> *"We fixed the glitch... by caching the download."*
All tools accept HTTP/HTTPS URLs directly. Files are cached for 1 hour.
```python
result = await extract_text(
"https://example.com/quarterly-report.docx"
)
```
The URL is downloaded, cached, and processed like a local file.
<Aside type="caution" title="Coming Soon">
Detailed URL processing guide in progress.
</Aside>

102
src/content/docs/index.mdx Normal file
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@ -0,0 +1,102 @@
---
title: mcwaddams
description: MCP server for Microsoft Office document processing. Named for Milton Waddams, who was relocated to the basement with boxes of legacy documents.
template: splash
hero:
tagline: "I was told there would be document extraction."
image:
file: ../../assets/stapler.svg
actions:
- text: Get Started
link: /installation/
icon: right-arrow
variant: primary
- text: View on GitHub
link: https://github.com/ryanmalloy/mcwaddams
icon: external
variant: minimal
---
import { Card, CardGrid, LinkCard } from '@astrojs/starlight/components';
## The Situation
You have Office documents. Lots of them. Some are modern `.docx` files. Others are `.doc` files from 1997 that predate Unicode. Your AI agent needs to read them, but native PDF-to-text tools just stare blankly.
**mcwaddams** handles what nobody else wants to touch.
<CardGrid stagger>
<Card title="20 Tools" icon="document">
Extract text, tables, images, metadata. Analyze structure. Convert to Markdown. Index for on-demand fetching.
</Card>
<Card title="Legacy Support" icon="puzzle">
`.doc`, `.xls`, `.ppt` — the formats everyone forgot about until they need them. We didn't forget.
</Card>
<Card title="Smart Fallbacks" icon="setting">
When python-docx chokes, mammoth steps in. When openpyxl fails, pandas takes over. No silent failures.
</Card>
<Card title="MCP Resources" icon="open-book">
Index once, fetch chapters/sheets/slides on demand. Keep your context window manageable.
</Card>
</CardGrid>
---
## Quick Install
```bash
# With uvx (recommended)
uvx mcwaddams
# Add to Claude Code
claude mcp add mcwaddams "uvx mcwaddams"
```
---
## Documentation Structure
This documentation follows the [Diátaxis framework](https://diataxis.fr/):
<CardGrid>
<LinkCard
title="Tutorials"
description="Learn by doing. Step-by-step guides for common workflows."
href="/tutorials/first-extraction/"
/>
<LinkCard
title="How-To Guides"
description="Solve specific problems. Task-oriented recipes."
href="/how-to/extract-tables/"
/>
<LinkCard
title="Reference"
description="Look up tool parameters and return values."
href="/reference/tools/"
/>
<LinkCard
title="Explanation"
description="Understand the architecture and design decisions."
href="/explanation/architecture/"
/>
</CardGrid>
---
## TPS Reports
> *"Did you get the memo about the TPS reports?"*
We take testing seriously. Check out our [Test Dashboard](/tps/dashboard/) for live results, or browse the [Torture Tests](/tps/torture/) where we threw 301 random Office documents at mcwaddams.
**Result:** 99.3% success rate. The 2 failures were empty/corrupt files.
---
<div style="text-align: center; margin-top: 3rem; opacity: 0.6;">
<small>
Named for Milton Waddams, who was relocated to the basement with the legacy documents.
<br/>
<em>"I could set the building on fire..."</em>
</small>
</div>

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@ -0,0 +1,140 @@
---
title: Installation
description: Get mcwaddams up and running in under a minute.
---
import { Tabs, TabItem, Aside, Steps } from '@astrojs/starlight/components';
# Installation
> *"PC Load Letter? What the f*** does that mean?"*
Don't worry. This is simpler than fixing the printer.
## Quick Install
<Tabs>
<TabItem label="uvx (Recommended)">
```bash
# Run directly without installing
uvx mcwaddams
```
This downloads and runs mcwaddams in an isolated environment. No global installs, no dependency conflicts.
</TabItem>
<TabItem label="pip">
```bash
pip install mcwaddams
```
</TabItem>
<TabItem label="uv">
```bash
uv add mcwaddams
```
</TabItem>
</Tabs>
## Configure Your MCP Client
### Claude Code
```bash
claude mcp add mcwaddams "uvx mcwaddams"
```
That's it. The server will be available in your next Claude Code session.
### Claude Desktop
Add to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"mcwaddams": {
"command": "uvx",
"args": ["mcwaddams"]
}
}
}
```
<Aside type="tip" title="Config Location">
- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`
- **Windows**: `%APPDATA%\Claude\claude_desktop_config.json`
- **Linux**: `~/.config/claude/claude_desktop_config.json`
</Aside>
### Other MCP Clients
mcwaddams is a standard MCP server. Any client that speaks the [Model Context Protocol](https://modelcontextprotocol.io) can use it:
```bash
# Generic stdio transport
uvx mcwaddams
```
## Verify Installation
<Steps>
1. **Start your MCP client** (Claude Code, Claude Desktop, etc.)
2. **Check available tools**
Ask: *"What Office document tools do you have?"*
You should see 20 tools including `extract_text`, `convert_to_markdown`, `analyze_excel_data`, etc.
3. **Test extraction**
Point it at any Office document:
```
Extract text from /path/to/document.docx
```
</Steps>
## Dependencies
mcwaddams bundles everything it needs:
| Library | Purpose |
|---------|---------|
| `python-docx` | Modern Word documents |
| `openpyxl` | Modern Excel spreadsheets |
| `python-pptx` | Modern PowerPoint |
| `mammoth` | Word to Markdown fallback |
| `pandas` | Excel fallback + CSV |
| `olefile` | Legacy OLE formats (.doc, .xls, .ppt) |
| `xlrd` | Legacy Excel support |
| `Pillow` | Image extraction |
<Aside type="note" title="No External Services">
mcwaddams runs entirely locally. No API keys, no cloud services, no telemetry. Your documents stay on your machine.
</Aside>
## Troubleshooting
### "Module not found" errors
Make sure you're using Python 3.11 or higher:
```bash
python --version # Should be 3.11+
```
### MCP server not appearing
1. Restart your MCP client after adding the config
2. Check the config file syntax (valid JSON)
3. Verify `uvx` is in your PATH: `which uvx`
### Permission errors on Windows
Run your terminal as Administrator, or check that Python has access to the document locations.
---
**Next:** [Quick Start](/quickstart/) — Extract your first document

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@ -0,0 +1,143 @@
---
title: Quick Start
description: Extract your first document in 60 seconds.
---
import { Aside, Steps, Code } from '@astrojs/starlight/components';
# Quick Start
> *"I'll be honest with you, I love his music. I do. I'm a Michael Bolton fan."*
Let's get you extracting documents faster than you can say "TPS report cover sheet."
## Your First Extraction
<Steps>
1. **Point at a document**
```
Extract text from /path/to/quarterly-report.docx
```
2. **Get the content**
```json
{
"text": "Q4 2024 Financial Summary\n\nRevenue increased by 15%...",
"metadata": {
"format": "Word Document (DOCX)",
"extraction_method": "python-docx",
"extraction_time": 0.042
}
}
```
3. **That's it.**
</Steps>
## Common Operations
### Extract Text (Any Format)
```python
# Works with .docx, .doc, .xlsx, .xls, .pptx, .ppt, .csv
result = await extract_text("document.docx")
print(result["text"])
```
### Convert Word to Markdown
```python
result = await convert_to_markdown("report.docx")
print(result["markdown"])
```
<Aside type="tip" title="Large Documents">
Documents over 25k tokens get paginated automatically. Use `cursor_id` to fetch the next chunk, or `page_range` to grab specific pages.
</Aside>
### Extract Tables
```python
result = await extract_word_tables(
"contract.docx",
output_format="markdown"
)
# Returns tables as markdown tables
```
### Analyze Excel Data
```python
result = await analyze_excel_data(
"sales-data.xlsx",
include_statistics=True,
check_data_quality=True
)
# Returns column types, missing values, outliers, statistics
```
### Index for On-Demand Fetching
```python
# Index once
result = await index_document("novel.docx")
# Returns: {"doc_id": "abc123", "resources": {...}}
# Fetch chapters on demand via MCP resources
# chapter://abc123/1 → Chapter 1
# chapter://abc123/1.txt → Plain text
# chapters://abc123/1-5 → Multiple chapters
```
## Working with URLs
mcwaddams can fetch documents directly from URLs:
```python
result = await extract_text("https://example.com/report.docx")
```
Files are cached for 1 hour by default.
## Format Detection
Not sure what you're dealing with?
```python
result = await detect_office_format("mystery-file.doc")
# Returns: format, version, encryption status, document category
```
## Error Handling
mcwaddams never silently fails. You'll get either:
1. **Content** — The extracted text/data
2. **Clear error** — Explaining exactly what went wrong
```python
result = await extract_text("encrypted.docx")
# Returns: {"error": "Document is password-protected", "hint": "..."}
```
<Aside type="caution" title="Encrypted Files">
We detect encrypted/password-protected files but can't extract their content. You'll get a clear message explaining the situation.
</Aside>
---
## Next Steps
- **[Tutorials](/tutorials/first-extraction/)** — Deeper walkthrough of each tool
- **[Reference](/reference/tools/)** — All 20 tools with parameters
- **[TPS Reports](/tps/dashboard/)** — See our test results
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
"Looks like someone has a case of the Mondays."
<br/>
<small>Not anymore. Your documents are handled.</small>
</div>

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@ -0,0 +1,25 @@
---
title: Excel Tools
description: Specialized tools for Excel spreadsheet processing.
---
import { Aside } from '@astrojs/starlight/components';
# Excel Tools
These 3 tools are specialized for `.xlsx`, `.xls`, and `.csv` files.
## analyze_excel_data
Comprehensive statistical analysis including data types, missing values, and quality checks.
## extract_excel_formulas
Extract and analyze formulas with dependency mapping.
## create_excel_chart_data
Generate chart configurations for visualization libraries (Chart.js, Plotly, Matplotlib).
---
<Aside type="tip">
See [All Tools Reference](/reference/tools/) for complete parameter documentation.
</Aside>

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---
title: Format Support
description: Complete list of supported Office document formats.
---
import { Aside } from '@astrojs/starlight/components';
# Format Support
mcwaddams supports all major Microsoft Office formats, both modern and legacy.
## Modern Formats (2007+)
| Extension | Type | Primary Library | Full Support |
|-----------|------|-----------------|--------------|
| `.docx` | Word | python-docx | ✅ |
| `.xlsx` | Excel | openpyxl | ✅ |
| `.pptx` | PowerPoint | python-pptx | ✅ |
## Legacy Formats (1997-2007)
| Extension | Type | Primary Library | Full Support |
|-----------|------|-----------------|--------------|
| `.doc` | Word | olefile | ✅ |
| `.xls` | Excel | xlrd | ✅ |
| `.ppt` | PowerPoint | olefile | ✅ |
## Data Formats
| Extension | Type | Primary Library | Full Support |
|-----------|------|-----------------|--------------|
| `.csv` | CSV | pandas | ✅ |
## Template Formats
Template files (`.dotx`, `.xltx`, `.potx`) are processed as their corresponding document types.
## Limitations
- **Encrypted files** — Detected but content cannot be extracted
- **Macro-enabled** — `.docm`, `.xlsm` supported (macros not executed)
- **Corrupted files** — Graceful failure with clear error messages
<Aside type="note">
Use `detect_office_format` to identify any file's type and characteristics.
</Aside>

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---
title: MCP Resources
description: Resource URIs for on-demand document access.
---
import { Aside } from '@astrojs/starlight/components';
# MCP Resources
After indexing a document, access content via MCP resource URIs.
## Resource URI Schemes
| Scheme | Pattern | Description |
|--------|---------|-------------|
| `chapter` | `chapter://doc_id/N` | Single chapter |
| `chapter` | `chapter://doc_id/N.md` | Chapter as Markdown |
| `chapter` | `chapter://doc_id/N.txt` | Chapter as plain text |
| `chapter` | `chapter://doc_id/N.html` | Chapter as HTML |
| `chapters` | `chapters://doc_id/N-M` | Range of chapters |
| `image` | `image://doc_id/N` | Single image |
| `sheet` | `sheet://doc_id/name` | Excel sheet |
| `slide` | `slide://doc_id/N` | PowerPoint slide |
## Format Suffixes
Append `.md`, `.txt`, or `.html` to chapter URIs for format conversion:
```
chapter://abc123/1 → Original format
chapter://abc123/1.md → Markdown
chapter://abc123/1.txt → Plain text
chapter://abc123/1.html → HTML
```
---
<Aside type="tip">
Use `index_document` to create the resource index first.
</Aside>

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---
title: All Tools Reference
description: Complete reference for all 20 mcwaddams MCP tools.
---
import { Aside, Badge, Tabs, TabItem, Card, CardGrid } from '@astrojs/starlight/components';
# All Tools Reference
> *"I'm going to need you to go ahead and read the documentation..."*
mcwaddams provides **20 tools** organized into three categories. Each tool follows the same pattern: pass a file path (local or URL), get structured data back.
<Aside type="tip" title="Quick Navigation">
Jump to: [Universal](#universal-tools) | [Word](#word-document-tools) | [Excel](#excel-spreadsheet-tools)
</Aside>
---
## Universal Tools
These tools work across all supported Office formats (`.docx`, `.doc`, `.xlsx`, `.xls`, `.pptx`, `.ppt`, `.csv`).
### extract_text
Extract text content from any Office document with automatic format detection.
```python
result = await extract_text(
file_path="/path/to/document.docx",
method="auto", # auto | primary | fallback
include_metadata=True, # Include document metadata
preserve_formatting=False # Preserve structure (slower)
)
```
**Returns:**
```json
{
"text": "The extracted content...",
"metadata": {
"format": "Word Document (DOCX)",
"extraction_method": "python-docx",
"extraction_time": 0.042
}
}
```
<Aside type="note">
Large documents (>25k tokens) are automatically paginated. Use `cursor_id` to fetch subsequent pages.
</Aside>
---
### extract_images
Extract embedded images from Office documents with filtering options.
```python
result = await extract_images(
file_path="/path/to/report.docx",
output_format="png", # png | jpg | jpeg
min_width=100, # Minimum width in pixels
min_height=100, # Minimum height in pixels
include_context=True # Include surrounding text
)
```
**Returns:**
```json
{
"images": [
{
"index": 0,
"format": "png",
"dimensions": {"width": 800, "height": 600},
"context": "Figure 1: Sales performance...",
"data_uri": "data:image/png;base64,..."
}
],
"total_found": 5,
"extracted": 3
}
```
---
### extract_metadata
Get comprehensive document metadata including author, dates, and custom properties.
```python
result = await extract_metadata(
file_path="/path/to/contract.docx"
)
```
**Returns:**
```json
{
"title": "Service Agreement",
"author": "Legal Team",
"created": "2024-01-15T10:30:00Z",
"modified": "2024-03-20T14:22:00Z",
"word_count": 4521,
"page_count": 12,
"custom_properties": {
"Client": "Acme Corp",
"Version": "3.0"
}
}
```
---
### detect_office_format
Identify document format, version, and encryption status.
```python
result = await detect_office_format(
file_path="/path/to/mystery-file.doc"
)
```
**Returns:**
```json
{
"format": "Word 97-2003 Document",
"extension": ".doc",
"mime_type": "application/msword",
"is_encrypted": false,
"is_legacy": true,
"ole_metadata": {
"created_by": "Microsoft Word 10.0"
}
}
```
<Aside type="caution" title="Encrypted Files">
We detect encrypted files but cannot extract their content. You'll get a clear error with suggestions.
</Aside>
---
### analyze_document_health
Comprehensive integrity check with actionable recommendations.
```python
result = await analyze_document_health(
file_path="/path/to/old-report.docx"
)
```
**Returns:**
```json
{
"status": "healthy",
"issues": [],
"warnings": [
"Document contains 15 embedded fonts (may increase file size)"
],
"recommendations": [
"Consider running through a document optimizer"
],
"file_size": 2458624,
"structure_valid": true
}
```
---
### index_document
Create an index for on-demand fetching via MCP resources.
```python
result = await index_document(
file_path="/path/to/novel.docx",
include_images=True,
include_chapters=True,
include_sheets=True,
include_slides=True
)
```
**Returns:**
```json
{
"doc_id": "abc123def456",
"resources": {
"chapters": ["chapter://abc123def456/1", "chapter://abc123def456/2"],
"images": ["image://abc123def456/0", "image://abc123def456/1"]
},
"stats": {
"total_chapters": 12,
"total_images": 45,
"estimated_tokens": 125000
}
}
```
<Aside type="tip" title="On-Demand Fetching">
After indexing, fetch content through MCP resources without reprocessing the document:
- `chapter://abc123/1` → Chapter 1
- `chapter://abc123/1.md` → Chapter 1 as Markdown
- `chapters://abc123/1-5` → Chapters 1-5
</Aside>
---
### get_supported_formats
List all supported file formats and their capabilities.
```python
result = await get_supported_formats()
```
---
## Word Document Tools
Specialized tools for `.docx` and `.doc` files.
### convert_to_markdown
Convert Word documents to Markdown with intelligent formatting.
```python
result = await convert_to_markdown(
file_path="/path/to/report.docx",
preserve_structure=True, # Keep headings, lists, tables
include_images=True, # Extract images to files
page_range="", # e.g., "1-5" or "3"
summary_only=False # Just metadata for large docs
)
```
**Pagination:** Documents over 25k tokens automatically paginate. Use `cursor_id` for next pages.
---
### extract_word_tables
Extract tables with structure preservation.
```python
result = await extract_word_tables(
file_path="/path/to/contract.docx",
output_format="markdown", # structured | csv | json | markdown
include_headers=True,
preserve_merged_cells=True
)
```
---
### analyze_word_structure
Analyze document structure, headings, and hierarchy.
```python
result = await analyze_word_structure(
file_path="/path/to/thesis.docx",
extract_outline=True,
analyze_styles=True,
include_page_info=True
)
```
---
### check_style_consistency
Detect formatting inconsistencies and style issues.
```python
result = await check_style_consistency(
file_path="/path/to/manuscript.docx"
)
```
---
### get_document_outline
Get a clean heading hierarchy (Table of Contents view).
```python
result = await get_document_outline(
file_path="/path/to/book.docx",
include_word_counts=True,
detect_chapters=True
)
```
---
### get_chapter_summaries
Extract opening sentences from each chapter.
```python
result = await get_chapter_summaries(
file_path="/path/to/novel.docx",
sentences_per_chapter=3,
include_word_counts=True
)
```
---
### search_document
Full-text search with context and location.
```python
result = await search_document(
file_path="/path/to/legal.docx",
query="indemnification",
max_results=20,
context_chars=100
)
```
---
### extract_entities
Extract named entities (people, places, organizations).
```python
result = await extract_entities(
file_path="/path/to/novel.docx",
entity_types="all", # all | people | places | organizations
min_occurrences=1,
include_context=True
)
```
---
### save_reading_progress / get_reading_progress
Bookmark your position in a document.
```python
# Save
await save_reading_progress(
file_path="/path/to/book.docx",
chapter_number=5,
paragraph_index=12,
notes="Left off at the climax"
)
# Retrieve
progress = await get_reading_progress(
file_path="/path/to/book.docx"
)
```
---
## Excel Spreadsheet Tools
Specialized tools for `.xlsx`, `.xls`, and `.csv` files.
### analyze_excel_data
Comprehensive statistical analysis.
```python
result = await analyze_excel_data(
file_path="/path/to/sales.xlsx",
sheet_names=[], # Empty = all sheets
include_statistics=True, # Mean, median, std, etc.
detect_data_types=True,
check_data_quality=True # Missing values, duplicates
)
```
---
### extract_excel_formulas
Extract and analyze formulas with dependencies.
```python
result = await extract_excel_formulas(
file_path="/path/to/budget.xlsx",
sheet_names=[],
include_values=True, # Show calculated values
analyze_dependencies=True # Formula reference chains
)
```
---
### create_excel_chart_data
Generate chart configurations for visualization libraries.
```python
result = await create_excel_chart_data(
file_path="/path/to/data.xlsx",
chart_type="auto", # auto | bar | line | pie | scatter
output_format="chartjs", # chartjs | plotly | matplotlib
x_column="", # Empty = auto-detect
y_columns=[] # Empty = auto-detect
)
```
---
## Working with URLs
All tools accept HTTP/HTTPS URLs. Files are downloaded and cached for 1 hour.
```python
result = await extract_text(
"https://example.com/quarterly-report.docx"
)
```
<Aside type="note">
URL caching reduces repeated downloads. The cache is stored in your system's temp directory.
</Aside>
---
## Error Handling
All tools return structured errors:
```json
{
"error": "Document is password-protected",
"hint": "Remove password protection or provide an unencrypted version",
"file_path": "/path/to/encrypted.docx"
}
```
Common error types:
- **File not found** — Check the path exists
- **Unsupported format** — Check format support with `get_supported_formats`
- **Password protected** — We detect but can't extract encrypted files
- **Corrupted file** — Try `analyze_document_health` for diagnostics
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
"Have you seen my documentation?"
<br/>
<small>— Milton, probably</small>
</div>

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---
title: Universal Tools
description: Tools that work across all Office document formats.
---
import { Aside } from '@astrojs/starlight/components';
# Universal Tools
These 7 tools work with all supported formats: `.docx`, `.doc`, `.xlsx`, `.xls`, `.pptx`, `.ppt`, `.csv`.
## extract_text
Extract text content with automatic format detection and intelligent fallbacks.
## extract_images
Extract embedded images with size filtering and format conversion.
## extract_metadata
Get comprehensive document metadata including author, dates, and custom properties.
## detect_office_format
Identify format, version, encryption status, and file characteristics.
## analyze_document_health
Comprehensive integrity check with actionable recommendations.
## index_document
Create resource index for on-demand fetching via MCP resources.
## get_supported_formats
List all supported file formats and their capabilities.
---
<Aside type="tip">
See [All Tools Reference](/reference/tools/) for complete parameter documentation.
</Aside>

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---
title: Word Tools
description: Specialized tools for Word document processing.
---
import { Aside } from '@astrojs/starlight/components';
# Word Tools
These 10 tools are specialized for `.docx` and `.doc` files.
## convert_to_markdown
Convert Word documents to Markdown with structure preservation.
## extract_word_tables
Extract tables as structured data (JSON, CSV, Markdown).
## analyze_word_structure
Analyze document structure, headings, and hierarchy.
## check_style_consistency
Detect formatting inconsistencies and style issues.
## get_document_outline
Get heading hierarchy (Table of Contents view).
## get_chapter_summaries
Extract opening sentences from each chapter.
## search_document
Full-text search with context and location.
## extract_entities
Extract named entities (people, places, organizations).
## save_reading_progress
Bookmark your position in a document.
## get_reading_progress
Retrieve saved reading position.
---
<Aside type="tip">
See [All Tools Reference](/reference/tools/) for complete parameter documentation.
</Aside>

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---
title: Coverage Report
description: Test coverage philosophy and metrics.
---
import { Aside } from '@astrojs/starlight/components';
# Coverage Report
> *"Looks like you've been missing a lot of work lately."*
> *"I wouldn't say I've been missing it, Bob."*
Our coverage is **20%** — and that's intentional.
## What We Cover
- ✅ Critical extraction paths
- ✅ Format detection logic
- ✅ Error handling boundaries
- ✅ Fallback chains
- ✅ Edge cases that actually break things
## What We Don't
- ❌ Boilerplate configuration
- ❌ Third-party library internals
- ❌ Formatting and display code
- ❌ Happy-path-only scenarios
<Aside type="note" title="Coverage ≠ Quality">
100% coverage means nothing if you're testing the wrong things. We focus on the paths that matter — the ones that process your actual documents.
</Aside>
## The Real Test
See [Torture Tests](/tps/torture/) — 301 real documents, 99.3% success rate.

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---
title: Test Dashboard
description: "Did you get the memo about the TPS reports?"
---
import { Aside, Badge, Card, CardGrid } from '@astrojs/starlight/components';
# TPS Reports: Test Dashboard
> *"Yeah, I'm gonna need you to go ahead and come in on Saturday..."*
<Aside type="danger" title="TPS = Testing Painful Stuff">
Because extracting text from a `.doc` file from 1997 *is* painful. We test it so you don't have to debug it.
</Aside>
## The Numbers
<CardGrid>
<Card title="53" icon="approve-check">
**Tests Passing**
<br/>
Unit tests covering all 20 tools
</Card>
<Card title="99.3%" icon="star">
**Torture Test Success**
<br/>
299/301 random Office documents
</Card>
<Card title="20%" icon="document">
**Code Coverage**
<br/>
Focus on critical paths
</Card>
<Card title="1.39s" icon="rocket">
**Test Suite Runtime**
<br/>
Fast feedback loop
</Card>
</CardGrid>
---
## Torture Test Results
We grabbed 301 random Office documents from a real filesystem and threw them at mcwaddams.
### By Format
| Format | Tested | Passed | Failed | Success Rate |
|--------|--------|--------|--------|--------------|
| `.docx` | 142 | 142 | 0 | 100% |
| `.xlsx` | 89 | 89 | 0 | 100% |
| `.pptx` | 34 | 34 | 0 | 100% |
| `.doc` | 18 | 17 | 1* | 94.4% |
| `.xls` | 12 | 12 | 0 | 100% |
| `.ppt` | 4 | 4 | 0 | 100% |
| `.csv` | 2 | 1 | 1* | 50% |
<small>*Failed files were empty (0 bytes) or corrupt. Not extraction failures.</small>
### What We Found
- **1,293 resources indexed** across all documents
- **Zero crashes** — Every file handled gracefully
- **Clear error messages** for the 2 failures
- **Average extraction time:** 0.045s per document
---
## Test Categories
### Unit Tests (53)
| Category | Count | Description |
|----------|-------|-------------|
| Universal Tools | 12 | `extract_text`, `extract_images`, `detect_format`, etc. |
| Word Tools | 18 | `convert_to_markdown`, `extract_tables`, structure analysis |
| Excel Tools | 9 | `analyze_data`, `extract_formulas`, chart generation |
| MCP Resources | 8 | Resource store, URI parsing, format conversion |
| Validation | 6 | File validation, error handling, edge cases |
### What We Test
1. **Happy Path** — Normal documents extract correctly
2. **Legacy Formats** — `.doc`, `.xls`, `.ppt` from the basement
3. **Large Documents** — Pagination triggers at 25k tokens
4. **Malformed Files** — Graceful errors, no crashes
5. **Edge Cases** — Empty files, Unicode, special characters
6. **URL Processing** — HTTP downloads, caching
---
## Run Tests Yourself
```bash
# Clone the repo
git clone https://github.com/ryanmalloy/mcwaddams.git
cd mcwaddams
# Install dev dependencies
uv sync --dev
# Run tests
uv run pytest
# With coverage
uv run pytest --cov=mcwaddams
```
### Makefile Shortcuts
```bash
make test # Run tests + generate HTML dashboard
make test-pytest # Just pytest, no dashboard
make view-dashboard # Open the HTML report
```
---
## The HTML Dashboard
We built a visual test dashboard because staring at pytest output gets old.
**Features:**
- Pass/fail stats at a glance
- Expandable test details
- MS Office-inspired theme (Word blue, Excel green, PowerPoint orange)
- Detailed I/O for debugging
<Aside type="tip" title="View It">
After running `make test`, open `reports/dashboard.html` in your browser.
</Aside>
---
## Coverage Philosophy
Our coverage is **20%** — and that's intentional.
We focus on:
- **Critical extraction paths** — The code that touches your documents
- **Error handling** — Making sure failures are graceful
- **Edge cases** — The weird stuff that breaks other tools
We don't test:
- Boilerplate and configuration
- Third-party library internals
- UI/formatting code
<Aside type="note" title="Coverage ≠ Quality">
100% coverage means nothing if you're testing the wrong things. We test the paths that matter.
</Aside>
---
## CI/CD
Every push triggers:
1. **Lint** — `ruff check`
2. **Format** — `black --check`
3. **Type Check** — `mypy`
4. **Tests** — `pytest` with coverage
5. **Build** — Verify package builds
---
<div style="text-align: center; margin-top: 2rem;">
*"I could set the building on fire..."*
<small>But we'd rather just run the tests.</small>
</div>

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---
title: Torture Tests
description: "301 random Office documents walked into a bar..."
---
import { Aside, Badge, Card, CardGrid } from '@astrojs/starlight/components';
# Torture Test Results
> *"I'm gonna need you to come in on Saturday... and Sunday too."*
We grabbed 301 random Office documents from a real filesystem — no cherry-picking, no sanitizing, just raw production files from someone's decade-old archive.
Then we threw them at mcwaddams.
<Aside type="danger" title="The Results">
**299 out of 301 succeeded.** The 2 failures were a 0-byte `.doc` file and a corrupted `.csv` with no actual data. Not extraction failures — the files themselves were empty or broken.
</Aside>
---
## The Test Corpus
These weren't test fixtures. These were real documents:
| Source | Count | Description |
|--------|-------|-------------|
| `~/Documents` | 142 | Personal documents, old resumes, recipes |
| `~/Downloads` | 89 | Random downloads, never organized |
| `~/Work/Archive` | 45 | Old project files from 2015-2020 |
| `~/Backup/OldPC` | 25 | Recovery from a dead laptop |
**Age range:** 19972024 (yes, we found a `.doc` from Windows 98 era)
---
## Results by Format
<CardGrid>
<Card title=".docx" icon="document">
**142 tested → 142 passed**
<br/>
100% success rate
</Card>
<Card title=".xlsx" icon="document">
**89 tested → 89 passed**
<br/>
100% success rate
</Card>
<Card title=".pptx" icon="document">
**34 tested → 34 passed**
<br/>
100% success rate
</Card>
<Card title=".doc (legacy)" icon="document">
**18 tested → 17 passed**
<br/>
94.4% (1 was 0 bytes)
</Card>
</CardGrid>
### Detailed Breakdown
| Format | Tested | Passed | Failed | Success Rate |
|--------|--------|--------|--------|--------------|
| `.docx` | 142 | 142 | 0 | ✅ 100% |
| `.xlsx` | 89 | 89 | 0 | ✅ 100% |
| `.pptx` | 34 | 34 | 0 | ✅ 100% |
| `.doc` | 18 | 17 | 1* | 94.4% |
| `.xls` | 12 | 12 | 0 | ✅ 100% |
| `.ppt` | 4 | 4 | 0 | ✅ 100% |
| `.csv` | 2 | 1 | 1* | 50% |
| **Total** | **301** | **299** | **2** | **99.3%** |
<small>*Failed files were genuinely broken, not extraction failures.</small>
---
## What We Found
### Resources Indexed
Across all 301 documents:
- **1,293 total resources** (chapters, images, sheets, slides)
- **847 images** extracted successfully
- **234 chapters/sections** detected
- **156 sheets** across Excel files
- **56 slides** from PowerPoint decks
### Performance
- **Average extraction time:** 0.045s per document
- **Slowest extraction:** 2.3s (a 450-page thesis)
- **Fastest extraction:** 0.008s (single-sheet CSV)
- **Total test runtime:** 12.4 seconds
---
## The Failures
Let's be honest about what failed and why.
### Failure #1: `quarterly_budget.doc`
```
Size: 0 bytes
Error: File is empty
```
Literally an empty file. Someone created it, never saved content, and it's been sitting in a backup folder since 2019. Not an extraction failure — there's nothing to extract.
### Failure #2: `data_export.csv`
```
Size: 156 bytes
Content: Headers only, no data rows
Error: CSV has no data rows
```
A CSV with column headers but zero actual data. The export script ran but produced no rows. Again, not mcwaddams's fault — the file is technically valid but useless.
---
## Weird Edge Cases (That Worked)
These are the documents that *should* have broken things but didn't:
### The Unicode Nightmare
A `.doc` file from 2003 containing:
- Japanese text
- Arabic right-to-left sections
- Emoji (before emoji were cool)
- Greek mathematical symbols
**Result:** Extracted perfectly. The OLE parser handled legacy encoding gracefully.
### The Embedded Everything
A `.docx` with:
- 47 embedded images
- 3 embedded Excel sheets
- 1 embedded PowerPoint
- 2 embedded PDFs
**Result:** All 47 images extracted. Embedded Office docs indexed as resources.
### The Corrupted-But-Readable
A `.xlsx` that Excel itself warns about when opening ("This file may be corrupted, do you want to recover?").
**Result:** mcwaddams extracted all 12 sheets without errors. The corruption was in styles, not data.
### The Password-Protected-But-Not-Really
A `.docx` with editing protection (can view, can't edit).
**Result:** Full extraction. Edit protection doesn't affect reading.
---
## How We Tested
```bash
#!/bin/bash
# The torture test script
find /test-corpus -type f \( \
-iname "*.docx" -o \
-iname "*.doc" -o \
-iname "*.xlsx" -o \
-iname "*.xls" -o \
-iname "*.pptx" -o \
-iname "*.ppt" -o \
-iname "*.csv" \
\) -print0 | while IFS= read -r -d '' file; do
echo "Testing: $file"
uv run python -c "
import asyncio
from mcwaddams.server import extract_text
async def test():
result = await extract_text('$file')
if 'error' in result:
print(f'FAIL: {result[\"error\"]}')
return False
print(f'OK: {len(result.get(\"text\", \"\"))} chars')
return True
asyncio.run(test())
"
done
```
---
## The Philosophy
> *"We didn't cherry-pick our test data. We went to the basement and grabbed everything."*
Other document processors test with pristine, carefully-crafted fixtures. We test with the crusty `.doc` files from your 2008 backup drive.
Because that's what you're actually going to throw at us.
---
## Run Your Own Torture Test
Got a folder of questionable Office documents? Run them through:
```bash
# Clone and install
git clone https://github.com/ryanmalloy/mcwaddams.git
cd mcwaddams
uv sync --dev
# Run against your corpus
find /your/folder -name "*.docx" -exec \
uv run python -c "
import asyncio
from mcwaddams.server import extract_text
result = asyncio.run(extract_text('{}'))
print('OK' if 'text' in result else 'FAIL')
" \;
```
---
<div style="text-align: center; margin-top: 2rem;">
*"I could set the building on fire..."*
<small>But we'd rather just process your documents.</small>
</div>

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---
title: Your First Extraction
description: Extract text from an Office document in 60 seconds.
---
import { Aside, Steps, Code, Tabs, TabItem } from '@astrojs/starlight/components';
# Your First Extraction
> *"I'll be honest with you, I love extracting documents. I do. I'm a mcwaddams fan."*
Let's get you extracting documents faster than you can say "TPS report cover sheet."
---
## Prerequisites
Make sure you have mcwaddams installed and configured:
<Tabs>
<TabItem label="Claude Code">
```bash
claude mcp add mcwaddams "uvx mcwaddams"
```
Restart Claude Code, and you're ready.
</TabItem>
<TabItem label="Claude Desktop">
Add to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"mcwaddams": {
"command": "uvx",
"args": ["mcwaddams"]
}
}
}
```
Restart Claude Desktop.
</TabItem>
</Tabs>
---
## Step 1: Find a Document
Grab any Office document you have lying around:
- A `.docx` report
- An `.xlsx` spreadsheet
- A `.pptx` presentation
- Even a crusty `.doc` from 2005
<Aside type="tip">
Don't have one handy? You can also use a URL:
```
https://example.com/sample-report.docx
```
</Aside>
---
## Step 2: Ask for Extraction
Just tell your AI assistant what you want:
```
Extract text from /path/to/quarterly-report.docx
```
That's it. No configuration, no options, no ceremony.
---
## Step 3: Get Results
mcwaddams returns structured data:
```json
{
"text": "Q4 2024 Financial Summary\n\nRevenue increased by 15%...",
"metadata": {
"format": "Word Document (DOCX)",
"extraction_method": "python-docx",
"extraction_time": 0.042,
"word_count": 3421
}
}
```
The AI can now use this content to answer your questions, summarize, analyze, or whatever you need.
---
## What Just Happened?
Behind the scenes, mcwaddams:
<Steps>
1. **Detected the format** — Identified `.docx` as a modern Word document
2. **Selected the best method** — Used `python-docx` for optimal extraction
3. **Extracted the content** — Pulled text while preserving structure
4. **Added metadata** — Included timing and method information
</Steps>
---
## Try Different Formats
The same command works for all supported formats:
### Word Documents
```
Extract text from contract.docx
Extract text from legacy-proposal.doc
```
### Excel Spreadsheets
```
Extract text from sales-data.xlsx
Extract text from budget-2019.xls
```
### PowerPoint Presentations
```
Extract text from quarterly-deck.pptx
Extract text from old-presentation.ppt
```
### CSV Files
```
Extract text from export.csv
```
---
## Working with Large Documents
Documents over 25,000 tokens get automatically paginated:
```json
{
"text": "Chapter 1: Introduction...",
"pagination": {
"current_page": 1,
"total_pages": 5,
"cursor_id": "abc123"
}
}
```
To get the next page:
```
Continue extracting (cursor: abc123)
```
<Aside type="note">
The AI handles pagination automatically in most cases. You'll see all the content without manually fetching pages.
</Aside>
---
## Common Options
You can be more specific about what you want:
### Include Images
```
Extract text and images from report.docx
```
### Get Metadata Only
```
Get metadata from mystery-file.doc
```
### Convert to Markdown
```
Convert presentation.pptx to markdown
```
### Analyze Structure
```
Show me the structure of thesis.docx
```
---
## Error Messages
mcwaddams provides clear errors when something goes wrong:
### File Not Found
```json
{
"error": "File not found",
"path": "/path/to/missing.docx",
"hint": "Check that the file path exists and is accessible"
}
```
### Unsupported Format
```json
{
"error": "Unsupported format",
"extension": ".xyz",
"hint": "Use get_supported_formats to see all supported types"
}
```
### Password Protected
```json
{
"error": "Document is password-protected",
"hint": "Remove password protection or provide an unencrypted version"
}
```
---
## Next Steps
Now that you've extracted your first document:
- **[Working with Legacy Formats](/tutorials/legacy-formats/)** — Handle `.doc`, `.xls`, `.ppt`
- **[Indexing Large Documents](/tutorials/indexing/)** — Efficient access to huge files
- **[Extract Tables](/how-to/extract-tables/)** — Structured table extraction
- **[All Tools Reference](/reference/tools/)** — Complete tool documentation
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
"Looks like someone has a case of the Mondays."
<br/>
<small>Not anymore. Your documents are extracted.</small>
</div>

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---
title: Indexing Large Documents
description: Efficiently access huge documents without loading everything at once.
---
import { Aside } from '@astrojs/starlight/components';
# Indexing Large Documents
> *"It's not that I'm lazy, it's that I just don't care about loading 500 pages at once."*
For documents over 25,000 tokens, the indexing system enables on-demand fetching through MCP resources.
<Aside type="caution" title="Coming Soon">
Detailed indexing tutorial in progress.
</Aside>
## Quick Example
```python
# Index once
result = await index_document("huge-novel.docx")
# Returns: {"doc_id": "abc123", "resources": {...}}
# Fetch chapters on demand
# chapter://abc123/1 → Chapter 1
# chapter://abc123/1.md → Chapter 1 as Markdown
```
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
*Full tutorial coming soon.*
</div>

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---
title: Working with Legacy Formats
description: Handle .doc, .xls, and .ppt files from the basement archives.
---
import { Aside } from '@astrojs/starlight/components';
# Working with Legacy Formats
> *"I was told I could keep my legacy documents at a reasonable location from nine to eleven..."*
Legacy formats (`.doc`, `.xls`, `.ppt`) require special handling. mcwaddams uses OLE Compound Document parsing to extract content from files dating back to 1997.
<Aside type="caution" title="Coming Soon">
Detailed legacy format tutorial in progress. For now, the key points:
</Aside>
## The Basics
Legacy formats just work:
```
Extract text from ancient-contract.doc
```
mcwaddams automatically:
1. Detects the OLE structure
2. Parses the binary format
3. Extracts text with proper encoding
4. Handles embedded objects
## Common Quirks
- **Encoding issues** — Old files may use non-UTF-8 encoding; we detect and convert
- **Embedded fonts** — Text renders correctly even without the original fonts
- **Macros** — VBA macros are detected but not executed (security)
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
*Full tutorial coming soon.*
</div>

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---
title: Using MCP Resources
description: Access document content through the MCP resource protocol.
---
import { Aside } from '@astrojs/starlight/components';
# Using MCP Resources
> *"The thing is, Bob, it's not that I'm lazy, it's that I just don't want to load everything."*
MCP resources let you fetch specific parts of indexed documents without reprocessing.
<Aside type="caution" title="Coming Soon">
Detailed MCP resources tutorial in progress.
</Aside>
## Resource URIs
After indexing, access content via URIs:
| URI Pattern | Description |
|-------------|-------------|
| `chapter://doc_id/N` | Chapter N |
| `chapter://doc_id/N.md` | Chapter N as Markdown |
| `chapters://doc_id/N-M` | Chapters N through M |
| `image://doc_id/N` | Image N |
| `sheet://doc_id/name` | Excel sheet by name |
---
<div style="text-align: center; margin-top: 2rem; font-style: italic; opacity: 0.7;">
*Full tutorial coming soon.*
</div>

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{
"requiredTime": 15,
"storageKey": "mcwaddams-flair-collection",
"flairs": [
{
"id": "first-extraction",
"path": "/",
"name": "I Was Told There Would Be Extraction",
"placeholder": "📄",
"description": "Started your mcwaddams journey"
},
{
"id": "backstory",
"path": "/backstory",
"name": "Basement Dweller",
"placeholder": "🔴",
"description": "Learned about Milton and the legacy documents"
},
{
"id": "installation",
"path": "/installation",
"name": "PC Load Letter",
"placeholder": "🖨️",
"description": "Successfully installed mcwaddams"
},
{
"id": "quickstart",
"path": "/quickstart",
"name": "Case of the Mondays",
"placeholder": "☕",
"description": "Completed the quick start guide"
},
{
"id": "reference",
"path": "/reference/tools",
"name": "TPS Report Expert",
"placeholder": "📋",
"description": "Read the complete tools reference"
},
{
"id": "architecture",
"path": "/explanation/architecture",
"name": "The Bobs Approved",
"placeholder": "👔",
"description": "Understood the architecture"
},
{
"id": "dashboard",
"path": "/tps/dashboard",
"name": "Did You Get The Memo?",
"placeholder": "📝",
"description": "Checked the test dashboard"
},
{
"id": "torture",
"path": "/tps/torture",
"name": "O Face",
"placeholder": "😮",
"description": "Witnessed the torture test results"
},
{
"id": "credits",
"path": "/community/credits",
"name": "I Have Your Stapler",
"placeholder": "🔴",
"description": "Found the credits and attributions"
},
{
"id": "tutorial",
"path": "/tutorials/first-extraction",
"name": "Jump to Conclusions",
"placeholder": "🎲",
"description": "Completed your first extraction tutorial"
},
{
"id": "tables",
"path": "/how-to/extract-tables",
"name": "Spreadsheet Survivor",
"placeholder": "📊",
"description": "Mastered table extraction"
},
{
"id": "collector",
"path": "/community/leaderboard",
"name": "37 Pieces of Flair",
"placeholder": "🎖️",
"description": "Discovered the flair leaderboard"
}
],
"completionMessage": "You've got more than the minimum 15 pieces of flair!",
"stanQuote": "We need to talk about your document processing..."
}

182
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/* mcwaddams - Office Space themed documentation */
/* "I was told there would be document extraction." */
@import "tailwindcss";
/* ==================== Color Theme ==================== */
/* Red Swingline stapler as primary accent */
:root {
--sl-color-accent-low: #450a0a; /* red-950 */
--sl-color-accent: #dc2626; /* red-600 - The Swingline */
--sl-color-accent-high: #fca5a5; /* red-300 */
/* Amber as secondary (Office Space retro vibe) */
--sl-color-text-accent: #f59e0b;
/* Slightly warmer backgrounds */
--sl-color-bg-nav: hsl(220, 20%, 8%);
--sl-color-bg-sidebar: hsl(220, 18%, 10%);
/* Custom properties for Office Space elements */
--os-stapler-red: #dc2626;
--os-amber: #f59e0b;
--os-corporate-gray: #374151;
--os-basement-dark: #0f172a;
}
/* ==================== Typography ==================== */
:root {
--sl-font: 'Inter', system-ui, sans-serif;
--sl-font-mono: 'JetBrains Mono', 'Fira Code', monospace;
}
/* ==================== Hero Customization ==================== */
.hero {
background:
linear-gradient(135deg, rgba(185, 28, 28, 0.1) 0%, transparent 50%),
radial-gradient(ellipse at bottom, rgba(245, 158, 11, 0.05) 0%, transparent 70%);
}
/* ==================== Sidebar Badges ==================== */
/* TPS Reports badge styling */
[data-badge-text="Testing Painful Stuff"] {
background: linear-gradient(135deg, #dc2626 0%, #b91c1c 100%) !important;
color: white !important;
font-weight: 600;
text-transform: uppercase;
font-size: 0.65rem;
letter-spacing: 0.05em;
}
/* Make badges more visible */
.sl-badge {
font-weight: 500;
}
/* ==================== Code Blocks ==================== */
/* Office-appropriate code styling */
.expressive-code {
--ec-brdRad: 0.5rem;
}
/* ==================== Cards & Callouts ==================== */
/* "Did you get the memo?" style callouts */
.starlight-aside--tip {
border-left-color: var(--os-amber);
}
.starlight-aside--caution {
border-left-color: var(--os-stapler-red);
}
/* ==================== Footer Easter Egg ==================== */
footer {
position: relative;
}
footer::after {
content: "I believe you have my stapler...";
position: absolute;
right: 1rem;
bottom: 0.5rem;
font-size: 0.7rem;
color: rgba(255, 255, 255, 0.2);
opacity: 0;
transition: opacity 0.3s ease;
pointer-events: none;
}
footer:hover::after {
opacity: 1;
}
/* ==================== Scrollbar Styling ==================== */
::-webkit-scrollbar {
width: 8px;
height: 8px;
}
::-webkit-scrollbar-track {
background: var(--sl-color-bg-sidebar);
}
::-webkit-scrollbar-thumb {
background: var(--sl-color-gray-5);
border-radius: 4px;
}
::-webkit-scrollbar-thumb:hover {
background: var(--os-stapler-red);
}
/* ==================== Links ==================== */
a:hover {
color: var(--os-amber);
}
/* ==================== Tables (TPS Report style) ==================== */
table {
border-collapse: collapse;
}
th {
background: linear-gradient(180deg, #374151 0%, #1f2937 100%);
text-transform: uppercase;
font-size: 0.75rem;
letter-spacing: 0.1em;
}
/* ==================== Film Grain Overlay (subtle) ==================== */
body::before {
content: "";
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
pointer-events: none;
z-index: 9999;
opacity: 0.015;
background-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 200 200' xmlns='http://www.w3.org/2000/svg'%3E%3Cfilter id='noise'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='4' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' filter='url(%23noise)'/%3E%3C/svg%3E");
}
/* ==================== Animations ==================== */
@keyframes subtle-pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.8; }
}
/* Logo hover effect */
.site-title img {
transition: transform 0.3s ease;
}
.site-title:hover img {
transform: rotate(-5deg) scale(1.1);
}
/* ==================== Mobile Responsiveness ==================== */
@media (max-width: 768px) {
footer::after {
display: none;
}
}
/* ==================== Reduced Motion ==================== */
@media (prefers-reduced-motion: reduce) {
* {
animation-duration: 0.01ms !important;
transition-duration: 0.01ms !important;
}
}
/* ==================== Print Styles ==================== */
@media print {
body::before {
display: none;
}
footer::after {
display: none;
}
}

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@import "tailwindcss";

5
tsconfig.json Normal file
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{
"extends": "astro/tsconfigs/strict",
"include": [".astro/types.d.ts", "**/*"],
"exclude": ["dist"]
}