Document Analysis Server

A Model Context Protocol (MCP) server that provides document analysis capabilities to LLM applications. Supports PDF, DOCX, and plaintext extraction with chunking, summarization, and semantic search.

Features

• Document Extraction - Parse PDF, DOCX, and TXT files into structured text
• Smart Chunking - Split documents with configurable overlap for RAG pipelines
• Semantic Search - Embed and search document chunks using Cohere Embed v3
• Summarization - Generate document summaries with configurable detail level
• Metadata Extraction - Extract titles, authors, dates, page counts
• MCP Protocol - Full MCP compliance for integration with Claude, IDEs, and other MCP hosts

Architecture

MCP Client (Claude, IDE, etc.)
        │
        │ MCP Protocol (JSON-RPC over stdio)
        │
        ▼
┌─────────────────────────────┐
│   MCP Document Server       │
├─────────────────────────────┤
│   Tools:                    │
│   ├── extract_text          │
│   ├── chunk_document        │
│   ├── search_chunks         │
│   ├── summarize_document    │
│   └── get_metadata          │
├─────────────────────────────┤
│   Resources:                │
│   ├── document://{path}     │
│   └── chunks://{doc_id}     │
├─────────────────────────────┤
│   Parsers:                  │
│   ├── PDFParser             │
│   ├── DocxParser            │
│   └── TextParser            │
└─────────────────────────────┘

Quick Start

Installation

git clone https://github.com/BabyChrist666/mcp-document-server.git
cd mcp-document-server

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

pip install -r requirements.txt

Usage with Claude Desktop

Add to your Claude Desktop claude_desktop_config.json:

{
  "mcpServers": {
    "document-analysis": {
      "command": "python",
      "args": ["-m", "mcp_doc_server"],
      "cwd": "/path/to/mcp-document-server"
    }
  }
}

Usage with Claude Code CLI

claude --mcp-server "python -m mcp_doc_server"

Tools

extract_text

Extract full text from a document file.

{
  "name": "extract_text",
  "arguments": {
    "file_path": "/path/to/document.pdf"
  }
}

chunk_document

Split a document into overlapping chunks for RAG.

{
  "name": "chunk_document",
  "arguments": {
    "file_path": "/path/to/document.pdf",
    "chunk_size": 500,
    "overlap": 50
  }
}

search_chunks

Semantic search across document chunks.

{
  "name": "search_chunks",
  "arguments": {
    "query": "What are the payment terms?",
    "doc_id": "contract_2024",
    "top_k": 5
  }
}

summarize_document

Generate a summary of a document.

{
  "name": "summarize_document",
  "arguments": {
    "file_path": "/path/to/report.pdf",
    "detail_level": "brief"
  }
}

get_metadata

Extract document metadata (title, author, pages, etc.).

{
  "name": "get_metadata",
  "arguments": {
    "file_path": "/path/to/document.pdf"
  }
}

Configuration

Variable	Description	Default
COHERE_API_KEY	Cohere API key for embeddings and generation	Required
EMBEDDING_MODEL	Cohere embedding model	embed-english-v3.0
CHUNK_SIZE	Default chunk size in characters	500
CHUNK_OVERLAP	Default overlap between chunks	50

Testing

pytest tests/ -v

Tech Stack

• Python 3.10+ - Runtime
• MCP SDK - Model Context Protocol implementation
• Cohere - Embeddings and generation
• PyPDF2 - PDF parsing
• python-docx - DOCX parsing
• Pydantic - Data validation

License

MIT