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MCP server for Chroma

What is chroma

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\<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

  1. Start the server:
uv run chroma
  1. 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
  1. Use the inspector's web interface to:
    • Test CRUD operations
    • Verify search functionality
    • Check error handling
    • Monitor server logs

Building

  1. Update dependencies:
uv compile pyproject.toml
  1. 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.

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.