Files-DB-MCP: Vector Search for Code Projects

A local vector database system that provides LLM coding agents with fast, efficient search capabilities for software projects via the Message Control Protocol (MCP).

Features

• Zero Configuration - Auto-detects project structure with sensible defaults
• Real-Time Monitoring - Continuously watches for file changes
• Vector Search - Semantic search for finding relevant code
• MCP Interface - Compatible with Claude Code and other LLM tools
• Open Source Models - Uses Hugging Face models for code embeddings

Installation

Option 1: Clone and Setup (Recommended)

# Using SSH (recommended if you have SSH keys set up with GitHub)
git clone git@github.com:randomm/files-db-mcp.git ~/.files-db-mcp && bash ~/.files-db-mcp/install/setup.sh

# Using HTTPS (if you don't have SSH keys set up)
git clone https://github.com/randomm/files-db-mcp.git ~/.files-db-mcp && bash ~/.files-db-mcp/install/setup.sh

Option 2: Automated Installation Script

curl -fsSL https://raw.githubusercontent.com/randomm/files-db-mcp/main/install/install.sh | bash

Usage

After installation, run in any project directory:

files-db-mcp

The service will:

1. Detect your project files
2. Start indexing in the background
3. Begin responding to MCP search queries immediately

Requirements

• Docker
• Docker Compose

Configuration

Files-DB-MCP works without configuration, but you can customize it with environment variables:

• EMBEDDING_MODEL - Change the embedding model (default: ‘jinaai/jina-embeddings-v2-base-code’ or project-specific model)
• FAST_STARTUP - Set to ‘true’ to use a smaller model for faster startup (default: ‘false’)
• QUANTIZATION - Enable/disable quantization (default: ‘true’)
• BINARY_EMBEDDINGS - Enable/disable binary embeddings (default: ‘false’)
• IGNORE_PATTERNS - Comma-separated list of files/dirs to ignore

First-Time Startup

On first run, Files-DB-MCP will download embedding models which may take several minutes depending on:

• The size of the selected model (300-500MB for high-quality models)
• Your internet connection speed

Subsequent startups will be much faster as models are cached in a persistent Docker volume. For faster initial startup, you can:

# Use a smaller, faster model (90MB)
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2 files-db-mcp

# Or enable fast startup mode
FAST_STARTUP=true files-db-mcp

Model Caching

Files-DB-MCP automatically persists downloaded embedding models, so you only need to download them once:

• Models are stored in a Docker volume called model_cache
• This volume persists between container restarts and across different projects
• The cache is shared for all projects using Files-DB-MCP on your machine
• You don’t need to download the model again for each project

Claude Code Integration

Add to your Claude Code configuration:

{
  "mcpServers": {
    "files-db-mcp": {
      "command": "python",
      "args": ["/path/to/src/claude_mcp_server.py", "--host", "localhost", "--port", "6333"]
    }
  }
}

For details, see Claude MCP Integration.

Documentation

• Installation Guide - Detailed setup instructions
• API Reference - Complete API documentation
• Configuration Guide - Configuration options

Repository Structure

• /src - Source code
• /tests - Unit and integration tests
• /docs - Documentation
• /scripts - Utility scripts
• /install - Installation scripts
• /.docker - Docker configuration
• /config - Configuration files
• /ai-assist - AI assistance files

License

MIT License

Contributing

Contributions welcome! Please feel free to submit a pull request.