MCP Document Analysis Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Document Analysis ServerSummarize the document report.pdf"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP 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
Related MCP server: doc-lib-mcp
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.txtUsage 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 for embeddings and generation | Required |
| Cohere embedding model |
|
| Default chunk size in characters |
|
| Default overlap between chunks |
|
Testing
pytest tests/ -vTech 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
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