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The Backstory

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: a document processing expert.

This MCP server channels that energy. It handles the legacy formats nobody else wants to touch. It extracts text from files that should have been migrated to Google Docs a decade ago. It reads the TPS reports.

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✨ Features

• Universal extraction — Pull text, images, and metadata from any Office format
• Format-specific tools — Deep analysis for Word (tables, structure), Excel (formulas, charts), PowerPoint
• Automatic pagination — Large documents get chunked so they don't blow up your context window
• Fallback processing — When one library chokes on a weird file, we try another
• URL support — Pass a URL instead of a file path; we'll download and cache it
• Legacy formats — Yes, even those .doc and .xls files from the basement

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🚀 Installation

# Quick install with uvx (recommended)
uvx mcwaddams

# Or install with uv/pip
uv add mcwaddams
pip install mcwaddams

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcwaddams": {
      "command": "uvx",
      "args": ["mcwaddams"]
    }
  }
}

Claude Code Configuration

claude mcp add mcwaddams "uvx mcwaddams"

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🛠 Available Tools

Universal Tools

Work with all Office formats: Word, Excel, PowerPoint, CSV

Tool	Description
extract_text	Extract text with optional formatting preservation
extract_images	Extract embedded images with size filtering
extract_metadata	Get document properties (author, dates, statistics)
detect_office_format	Identify format, version, encryption status
analyze_document_health	Check integrity, corruption, password protection
get_supported_formats	List all supported file extensions
index_document	Scan document and create resource URIs for on-demand fetching

Word Tools

Tool	Description
convert_to_markdown	Convert to Markdown with automatic pagination for large docs
extract_word_tables	Extract tables as structured JSON, CSV, or Markdown
analyze_word_structure	Analyze headings, sections, styles, and document hierarchy
get_document_outline	Get structured outline with chapter detection and word counts
check_style_consistency	Find formatting issues, missing chapters, style problems
search_document	Search text with context and chapter location
extract_entities	Extract people, places, organizations using pattern recognition
get_chapter_summaries	Generate chapter previews with opening sentences
save_reading_progress	Bookmark your reading position for later
get_reading_progress	Resume reading from saved position

Excel Tools

Tool	Description
analyze_excel_data	Statistical analysis: data types, missing values, outliers
extract_excel_formulas	Extract formulas with values and dependency analysis
create_excel_chart_data	Generate Chart.js/Plotly-ready data from spreadsheets

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📋 Format Support

Here's what works and what's "good enough" — legacy formats from Office 97-2003 have more limited extraction, but they still work:

Format	Extension	Text	Images	Metadata	Tables	Formulas
Word (Modern)	.docx	✅	✅	✅	✅	-
Word (Legacy)	.doc	✅	⚠️	⚠️	⚠️	-
Word Template	.dotx	✅	✅	✅	✅	-
Word Macro	.docm	✅	✅	✅	✅	-
Excel (Modern)	.xlsx	✅	✅	✅	✅	✅
Excel (Legacy)	.xls	✅	⚠️	⚠️	✅	⚠️
Excel Template	.xltx	✅	✅	✅	✅	✅
Excel Macro	.xlsm	✅	✅	✅	✅	✅
PowerPoint (Modern)	.pptx	✅	✅	✅	✅	-
PowerPoint (Legacy)	.ppt	✅	⚠️	⚠️	⚠️	-
PowerPoint Template	.potx	✅	✅	✅	✅	-
CSV	.csv	✅	-	⚠️	✅	-

✅ Full support • ⚠️ Basic/partial support • - Not applicable

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🔗 MCP Resources

Instead of returning entire documents in tool responses, you can index a document once and fetch content on-demand via URI-based resources. This keeps context windows manageable when working with large files.

How It Works

1. Index the document — index_document scans the file and returns URIs
2. Fetch what you need — Request specific chapters, sheets, slides, or images by URI
3. Format on demand — Append .txt or .html to get different output formats

Resource URI Patterns

URI Pattern	Description	Example
chapter://{doc_id}/{n}	Single chapter/section	chapter://abc123/3
chapters://{doc_id}/{range}	Multiple chapters	chapters://abc123/1-5
section://{doc_id}/{n}	Section by heading style	section://abc123/2
paragraph://{doc_id}/{ch}/{p}	Specific paragraph	paragraph://abc123/3/7
sheet://{doc_id}/{name}	Excel sheet as markdown table	sheet://abc123/Revenue
slide://{doc_id}/{n}	PowerPoint slide	slide://abc123/5
slides://{doc_id}/{range}	Multiple slides	slides://abc123/1,3,5
image://{doc_id}/{n}	Embedded image	image://abc123/0

Format Suffixes

Append a format suffix to convert on the fly:

Suffix	Output
.md (default)	Markdown
.txt	Plain text (no formatting)
.html	Basic HTML

Examples:

• chapter://abc123/3 → Markdown (default)
• chapter://abc123/3.txt → Plain text
• chapter://abc123/3.html → HTML

Range Syntax

Fetch multiple items at once:

• 1-5 → Items 1 through 5
• 1,3,5 → Specific items
• 1-3,7,9-10 → Mixed ranges

Section Detection

The indexer detects document structure automatically:

1. Heading 1 styles (primary) — Business docs, manuals, technical documents
2. "Chapter X" text patterns (fallback) — Books, manuscripts, narratives

Use text_patterns_only=True to skip heading style detection for documents with messy formatting.

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🎯 MCP Prompts

Pre-built workflows that chain multiple tools together:

Prompt	Level	Description
explore-document	Basic	Start with any new document - get structure and identify issues
find-character	Basic	Track all mentions of a person/character with context
chapter-preview	Basic	Quick overview of each chapter without full read
resume-reading	Intermediate	Check saved position and continue reading
document-analysis	Intermediate	Comprehensive multi-tool analysis
character-journey	Advanced	Track character arc through entire narrative
document-comparison	Advanced	Compare entities and themes between chapters
full-reading-session	Advanced	Guided reading with bookmarking
manuscript-review	Advanced	Complete editorial workflow for editors

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💡 Usage Examples

Extract Text from Any Document

# Simple extraction
result = await extract_text("report.docx")
print(result["text"])

# With formatting preserved
result = await extract_text(
    file_path="report.docx",
    preserve_formatting=True,
    include_metadata=True
)

Convert Word to Markdown (with Pagination)

Large documents get paginated automatically. Three ways to handle it:

# Option 1: Follow the cursor for each chunk
result = await convert_to_markdown("big-manual.docx")
if result.get("pagination", {}).get("has_more"):
    next_page = await convert_to_markdown(
        "big-manual.docx",
        cursor_id=result["pagination"]["cursor_id"]
    )

# Option 2: Grab specific pages
result = await convert_to_markdown("big-manual.docx", page_range="1-10")

# Option 3: Extract by chapter heading
result = await convert_to_markdown("big-manual.docx", chapter_name="Introduction")

Analyze Excel Data Quality

result = await analyze_excel_data(
    file_path="sales-data.xlsx",
    include_statistics=True,
    check_data_quality=True
)

# Returns per-column analysis with quality issues

Index Document for On-Demand Resource Fetching

# Index the document - returns URIs for all content
result = await index_document("novel.docx")

# Returns:
# {
#   "doc_id": "56036b0f171a",
#   "resources": {
#     "chapter": [
#       {"id": "1", "title": "Chapter 1", "uri": "chapter://56036b0f171a/1"},
#       ...
#     ],
#     "image": [
#       {"id": "0", "uri": "image://56036b0f171a/0"},
#       ...
#     ]
#   }
# }

# Fetch specific content via MCP resources:
# - chapter://56036b0f171a/1      → Chapter 1 as markdown
# - chapter://56036b0f171a/1.txt  → Chapter 1 as plain text
# - chapters://56036b0f171a/1-3   → Chapters 1-3 combined

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🧪 Testing

# Run tests and generate the dashboard
make test

# Just pytest
make test-pytest

# Open dashboard
make view-dashboard

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🏗 Architecture

The mixin pattern keeps things modular — universal tools work on everything, format-specific tools go deeper.

mcwaddams/
├── src/mcwaddams/
│   ├── server.py              # FastMCP server + resource templates
│   ├── resources.py           # Resource store for on-demand content
│   ├── mixins/
│   │   ├── universal.py       # Format-agnostic tools
│   │   ├── word.py            # Word-specific tools
│   │   ├── excel.py           # Excel-specific tools
│   │   └── powerpoint.py      # PowerPoint tools
│   ├── utils/                 # Validation, caching, detection
│   └── pagination.py          # Large document pagination
├── tests/
└── reports/

Processing Libraries

Format	Primary Library	Fallback
.docx	python-docx	mammoth
.xlsx	openpyxl	pandas
.pptx	python-pptx	-
.doc/.xls/.ppt	olefile	-
.csv	pandas	built-in csv

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🔧 Development

git clone https://github.com/ryanmalloy/mcwaddams.git
cd mcwaddams
uv sync --dev

uv run pytest
uv run black src/ tests/
uv run ruff check src/ tests/

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👤 Author

Ryan Malloy — ryanmalloy.com

This package emerged from a human-AI collaboration session. The process raised questions about discernment, voice, and what makes tools actually useful:

• AI Isn't New. Your Discernment Is What Matters. — 40 years of writing code and why discernment matters more than the tools

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📜 License

MIT License - see LICENSE for details.

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