chroma

A Model Context Protocol (MCP) server implementation that provides vector database capabilities through Chroma. This server enables semantic document search, metadata filtering, and document management with persistent storage.

Requirements

• Python 3.8+
• Chroma 0.4.0+
• MCP SDK 0.1.0+

Components

Resources

The server provides document storage and retrieval through Chroma's vector database: - Stores documents with content and metadata - Persists data in src/chroma/data directory - Supports semantic similarity search

Tools

The server implements CRUD operations and search functionality:

Document Management

• create_document: Create a new document
• Required: document_id, content
• Optional: metadata (key-value pairs)
• Returns: Success confirmation

• Error: Already exists, Invalid input

• read_document: Retrieve a document by ID

• Required: document_id
• Returns: Document content and metadata

• Error: Not found

• update_document: Update an existing document

• Required: document_id, content
• Optional: metadata
• Returns: Success confirmation

• Error: Not found, Invalid input

• delete_document: Remove a document

• Required: document_id
• Returns: Success confirmation

• Error: Not found

• list_documents: List all documents

• Optional: limit, offset
• Returns: List of documents with content and metadata

Search Operations

• search_similar: Find semantically similar documents
• Required: query
• Optional: num_results, metadata_filter, content_filter
• Returns: Ranked list of similar documents with distance scores
• Error: Invalid filter

Features

• Semantic Search: Find documents based on meaning using Chroma's embeddings
• Metadata Filtering: Filter search results by metadata fields
• Content Filtering: Additional filtering based on document content
• Persistent Storage: Data persists in local directory between server restarts
• Error Handling: Comprehensive error handling with clear messages
• Retry Logic: Automatic retries for transient failures

Installation

1. Install dependencies:

   uv venv
   uv sync --dev --all-extras

Configuration

Claude Desktop

Add the server configuration to your Claude Desktop config:

Windows: C:Users<username>AppDataRoamingClaudeclaude_desktop_config.json

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "chroma": {
      "command": "uv",
      "args": [
        "--directory",
        "C:/MCP/server/community/chroma",
        "run",
        "chroma"
      ]
    }
  }
}

Data Storage

The server stores data in: - Windows: src/chroma/data - MacOS/Linux: src/chroma/data

Usage

1. Start the server:

   uv run chroma

2. Use MCP tools to interact with the server:

# Create a document
create_document({
    "document_id": "ml_paper1",
    "content": "Convolutional neural networks improve image recognition accuracy.",
    "metadata": {
        "year": 2020,
        "field": "computer vision",
        "complexity": "advanced"
    }
})

# Search similar documents
search_similar({
    "query": "machine learning models",
    "num_results": 2,
    "metadata_filter": {
        "year": 2020,
        "field": "computer vision"
    }
})

Error Handling

The server provides clear error messages for common scenarios: - Document already exists [id=X] - Document not found [id=X] - Invalid input: Missing document_id or content - Invalid filter - Operation failed: [details]

Development

Testing

1. Run the MCP Inspector for interactive testing:

   npx @modelcontextprotocol/inspector uv --directory C:/MCP/server/community/chroma run chroma

2. Use the inspector's web interface to:

3. Test CRUD operations
4. Verify search functionality
5. Check error handling
6. Monitor server logs

Building

1. Update dependencies:

   uv compile pyproject.toml

2. Build package:

   uv build

Contributing

Contributions are welcome! Please read our Contributing Guidelines for details on: - Code style - Testing requirements - Pull request process

License

This project is licensed under the MIT License - see the LICENSE file for details.