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Chroma/chroma-mcp

Chroma MCP Server

    Server Summary

    • Manage collections of vector data

    • Perform semantic searches

    • Advanced data filtering

    • Retrieve data for AI models

    • Integrate with Python and JavaScript applications

Chroma - the open-source embedding database. 
The fastest way to build Python or JavaScript LLM apps with memory!

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Chroma MCP Server

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The Model Context Protocol (MCP) is an open protocol designed for effortless integration between LLM applications and external data sources or tools, offering a standardized framework to seamlessly provide LLMs with the context they require.

This server provides data retrieval capabilities powered by Chroma, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, metadata filtering, and more.

Features

  • Flexible Client Types

    • Ephemeral (in-memory) for testing and development
    • Persistent for file-based storage
    • HTTP client for self-hosted Chroma instances
    • Cloud client for Chroma Cloud integration (automatically connects to api.trychroma.com)

  • Collection Management

    • Create, modify, and delete collections
    • List all collections with pagination support
    • Get collection information and statistics
    • Configure HNSW parameters for optimized vector search
    • Select embedding functions when creating collections

  • Document Operations

    • Add documents with optional metadata and custom IDs
    • Query documents using semantic search
    • Advanced filtering using metadata and document content
    • Retrieve documents by IDs or filters
    • Full text search capabilities

Supported Tools

  • chroma_list_collections - List all collections with pagination support
  • chroma_create_collection - Create a new collection with optional HNSW configuration
  • chroma_peek_collection - View a sample of documents in a collection
  • chroma_get_collection_info - Get detailed information about a collection
  • chroma_get_collection_count - Get the number of documents in a collection
  • chroma_modify_collection - Update a collection's name or metadata
  • chroma_delete_collection - Delete a collection
  • chroma_add_documents - Add documents with optional metadata and custom IDs
  • chroma_query_documents - Query documents using semantic search with advanced filtering
  • chroma_get_documents - Retrieve documents by IDs or filters with pagination
  • chroma_update_documents - Update existing documents' content, metadata, or embeddings
  • chroma_delete_documents - Delete specific documents from a collection

Embedding Functions

Chroma MCP supports several embedding functions: default, cohere, openai, jina, voyageai, and roboflow.

The embedding functions utilize Chroma's collection configuration, which persists the selected embedding function of a collection for retrieval. Once a collection is created using the collection configuration, on retrieval for future queries and inserts, the same embedding function will be used, without needing to specify the embedding function again. Embedding function persistance was added in v1.0.0 of Chroma, so if you created a collection using version