MCP Document Indexer

A Python-based MCP (Model Context Protocol) server for local document indexing and search using LanceDB vector database and local LLMs.

Features

Real-time Document Monitoring: Automatically indexes new and modified documents in configured folders
Multi-format Support: Handles PDF, Word (docx/doc), text, Markdown, and RTF files
Local LLM Integration: Uses Ollama for document summarization and keyword extraction. Nothing ever leaves your computer
Vector Search: Semantic search using LanceDB and sentence transformers
MCP Integration: Exposes search and catalog tools via Model Context Protocol
Incremental Indexing: Only processes changed files to save resources
Performance Optimized: Designed for decent performance on standard laptops (e.g. M1/M2 MacBook)

Installation

Prerequisites

Python 3.9+ installed
uv package manager:

curl -LsSf https://astral.sh/uv/install.sh | sh

Ollama (for local LLM):

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model (e.g., llama3.2)
ollama pull llama3.2:3b

Install MCP Document Indexer

# Clone the repository
git clone https://github.com/yairwein/mcp-doc-indexer.git
cd mcp-doc-indexer

# Install with uv
uv sync

# Or install as a package
uv add mcp-doc-indexer

Configuration

Configure the indexer using environment variables or a .env file:

# Folders to monitor (comma-separated)
WATCH_FOLDERS="/Users/me/Documents,/Users/me/Research"

# LanceDB storage path
LANCEDB_PATH="./vector_index"

# Ollama model for summarization
LLM_MODEL="llama3.2:3b"

# Text chunking settings
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

# Embedding model (sentence-transformers)
EMBEDDING_MODEL="all-MiniLM-L6-v2"

# File types to index
FILE_EXTENSIONS=".pdf,.docx,.doc,.txt,.md,.rtf"

# Maximum file size in MB
MAX_FILE_SIZE_MB=100

# Ollama API URL
OLLAMA_BASE_URL="http://localhost:11434"

Usage

Run as Standalone Service

# Set environment variables
export WATCH_FOLDERS="/path/to/documents"
export LANCEDB_PATH="./my_index"

# Run the indexer
uv run python -m src.main

Integrate with Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "doc-indexer": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/mcp-doc-indexer",
        "python",
        "-m",
        "src.main"
      ],
      "env": {
        "WATCH_FOLDERS": "/Users/me/Documents,/Users/me/Research",
        "LANCEDB_PATH": "/Users/me/.mcp-doc-index",
        "LLM_MODEL": "llama3.2:3b"
      }
    }
  }
}

MCP Tools

The indexer exposes the following tools via MCP:

`search_documents`

Search for documents using natural language queries.

Parameters:
- query: Search query text
- limit: Maximum number of results (default: 10)
- search_type: "documents" or "chunks"

`get_catalog`

List all indexed documents with summaries.

Parameters:
- skip: Number of documents to skip (default: 0)
- limit: Maximum documents to return (default: 100)

`get_document_info`

Get detailed information about a specific document.

Parameters:
- file_path: Path to the document

`reindex_document`

Force reindexing of a specific document.

Parameters:
- file_path: Path to the document to reindex

`get_indexing_stats`

Get current indexing statistics.

Example Usage in Claude

Once configured, you can use the indexer in Claude:

"Search my documents for information about machine learning"
"Show me all PDFs I've indexed"
"What documents mention Python programming?"
"Get details about /Users/me/Documents/report.pdf"
"Reindex the latest version of my thesis"

Architecture

┌─────────────────┐     ┌──────────────┐     ┌─────────────┐
│  File Monitor   │────▶│   Document   │────▶│  Local LLM  │
│   (Watchdog)    │     │    Parser    │     │  (Ollama)   │
└─────────────────┘     └──────────────┘     └─────────────┘
                               │                      │
                               ▼                      ▼
                        ┌──────────────┐     ┌─────────────┐
                        │   LanceDB    │◀────│  Embeddings │
                        │   Storage    │     │  (ST Model) │
                        └──────────────┘     └─────────────┘
                               │
                               ▼
                        ┌──────────────┐
                        │  FastMCP     │
                        │   Server     │
                        └──────────────┘
                               │
                               ▼
                        ┌──────────────┐
                        │    Claude    │
                        │   Desktop    │
                        └──────────────┘

File Processing Pipeline

File Detection: Watchdog monitors configured folders for changes
Document Parsing: Extracts text from PDF, Word, and text files
Text Chunking: Splits documents into overlapping chunks for better retrieval
LLM Processing: Generates summaries and extracts keywords using Ollama
Embedding Generation: Creates vector embeddings using sentence transformers
Vector Storage: Stores documents and chunks in LanceDB
MCP Exposure: Makes search and catalog tools available via MCP

Performance Considerations

Incremental Indexing: Only changed files are reprocessed
Async Processing: Parallel processing of multiple documents
Batch Operations: Efficient batch indexing for multiple files
Debouncing: Prevents duplicate processing of rapidly changing files
Size Limits: Configurable maximum file size to prevent memory issues

Troubleshooting

Ollama Not Available

If Ollama is not running or the model isn't available, the indexer falls back to simple text extraction without summarization.

# Check Ollama status
ollama list

# Pull required model
ollama pull llama3.2:3b

Permission Issues

Ensure the indexer has read access to monitored folders:

chmod -R 755 /path/to/documents

Memory Usage

For large document collections, consider:

Reducing CHUNK_SIZE to create smaller chunks
Limiting MAX_FILE_SIZE_MB to skip very large files
Using a smaller embedding model

Development

Running Tests

uv run pytest tests/

Code Formatting

uv run black src/
uv run ruff src/

Building Package

uv build

License

MIT License - See LICENSE file for details

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch
Add tests for new functionality
Submit a pull request

Support

For issues or questions:

Open an issue on GitHub
Check the troubleshooting section
Review logs in the console output

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

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