Chroma MCP Server

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\\AppData\Roaming\Claude\claude_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:

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.