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

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>\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

Start the server:

uv run chroma

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

Run the MCP Inspector for interactive testing:

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

Use the inspector's web interface to:
- Test CRUD operations
- Verify search functionality
- Check error handling
- Monitor server logs

Building

Update dependencies:

uv compile pyproject.toml

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.