Chroma MCP Server

What is Chroma MCP Server about?

Provides a Model Context Protocol (MCP) server that wraps Chroma, an open‑source embedding database. It lets LLM applications create, query, and manage vector collections through a standardized protocol, enabling seamless context retrieval.

How to use Chroma MCP Server?

1. Run the server – typically via uvx chroma-mcp (or install with pip install chroma-mcp and execute chroma-mcp).
2. Configure the client – choose one of the client types:
   • Ephemeral – in‑memory, suitable for testing.
   • Persistent – file‑based storage using --data-dir.
   • HTTP – connect to a self‑hosted Chroma instance (--host, --port, --ssl, etc.).
   • Cloud – connect to Chroma Cloud (--tenant, --database, --api-key).
3. Set environment variables (or a .env file) for defaults; command‑line arguments override them.
4. Invoke tools – the server registers MCP tools such as chroma_create_collection, chroma_add_documents, chroma_query_documents, etc., which can be called by LLM agents or client libraries.

Key features of Chroma MCP Server

• Multiple client modes (ephemeral, persistent, HTTP, cloud) for flexible deployment.
• Full collection lifecycle: create, list (with pagination), modify, peek, get stats, delete.
• Document operations: add, update, delete, retrieve by ID or filter, semantic query, full‑text search, metadata filtering.
• Configurable HNSW parameters for optimized vector search.
• Embedding function support: default, cohere, openai, jina, voyageai, roboflow, with persistent configuration per collection.
• Environment‑driven configuration for API keys and client settings.
• Integration examples for Claude Desktop and other LLM frameworks.

Use cases of Chroma MCP Server

• LLM memory layers – store user interactions and generated content, enabling recall across sessions.
• Knowledge bases – ingest documents, articles, code snippets, and query them with semantic similarity.
• RAG pipelines – retrieve relevant chunks for prompt augmentation in retrieval‑augmented generation.
• Personal assistants – maintain dynamic context windows that evolve with user input.
• Prototype testing – use the in‑memory client for rapid iteration without persisting data.

FAQ from the Chroma MCP Server

Q: Do I need an API key for embedding functions? A: Yes, for external providers (Cohere, OpenAI, Jina, VoyageAI, Roboflow) set the corresponding CHROMA_<PROVIDER>_API_KEY environment variable.

Q: Can I switch embedding functions after a collection is created? A: The embedding function is persisted with the collection configuration; to change it you must create a new collection.

Q: How do I persist data across restarts? A: Use the persistent client type and specify a --data-dir path, or connect to Chroma Cloud/HTTP which store data externally.

Q: What authentication is required for the HTTP client? A: Provide --custom-auth-credentials (or the related env var) and optionally enable SSL via --ssl true.

Q: How do I control pagination when listing collections? A: The chroma_list_collections tool accepts limit and offset parameters to page through results.

Chroma MCP Server

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 <=0.6.3, this feature is not supported.

When accessing embedding functions that utilize external APIs, please be sure to add the environment variable for the API key with the correct format, found in Embedding Function Environment Variables

Usage with Claude Desktop

1. To add an ephemeral client, add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
        "chroma-mcp"
    ]
}

2. To add a persistent client, add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
        "chroma-mcp",
        "--client-type",
        "persistent",
        "--data-dir",
        "/full/path/to/your/data/directory"
    ]
}

This will create a persistent client that will use the data directory specified.

3. To connect to Chroma Cloud, add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
        "chroma-mcp",
        "--client-type",
        "cloud",
        "--tenant",
        "your-tenant-id",
        "--database",
        "your-database-name",
        "--api-key",
        "your-api-key"
    ]
}

This will create a cloud client that automatically connects to api.trychroma.com using SSL.

Note: Adding API keys in arguments is fine on local devices, but for safety, you can also specify a custom path for your environment configuration file using the --dotenv-path argument within the args list, for example: "args": ["chroma-mcp", "--dotenv-path", "/custom/path/.env"].

4. To connect to a [self-hosted Chroma instance on your own cloud provider](https://docs.trychroma.com/ production/deployment), add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
      "chroma-mcp", 
      "--client-type", 
      "http", 
      "--host", 
      "your-host", 
      "--port", 
      "your-port", 
      "--custom-auth-credentials",
      "your-custom-auth-credentials",
      "--ssl",
      "true"
    ]
}

This will create an HTTP client that connects to your self-hosted Chroma instance.

Demos

Find reference usages, such as shared knowledge bases & adding memory to context windows in the Chroma MCP Docs

Using Environment Variables

You can also use environment variables to configure the client. The server will automatically load variables from a .env file located at the path specified by --dotenv-path (defaults to .chroma_env in the working directory) or from system environment variables. Command-line arguments take precedence over environment variables.

# Common variables
export CHROMA_CLIENT_TYPE="http"  # or "cloud", "persistent", "ephemeral"

# For persistent client
export CHROMA_DATA_DIR="/full/path/to/your/data/directory"

# For cloud client (Chroma Cloud)
export CHROMA_TENANT="your-tenant-id"
export CHROMA_DATABASE="your-database-name"
export CHROMA_API_KEY="your-api-key"

# For HTTP client (self-hosted)
export CHROMA_HOST="your-host"
export CHROMA_PORT="your-port"
export CHROMA_CUSTOM_AUTH_CREDENTIALS="your-custom-auth-credentials"
export CHROMA_SSL="true"

# Optional: Specify path to .env file (defaults to .chroma_env)
export CHROMA_DOTENV_PATH="/path/to/your/.env" 

Embedding Function Environment Variables

When using external embedding functions that access an API key, follow the naming convention CHROMA_<>_API_KEY="<key>". So to set a Cohere API key, set the environment variable CHROMA_COHERE_API_KEY="". We recommend adding this to a .env file somewhere and using the CHROMA_DOTENV_PATH environment variable or --dotenv-path flag to set that location for safekeeping.