MCP server providing vector and full-text search over local or cloud Chroma embedding database instances.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent ā or use 1-click editor setup below.
š” Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Chroma MCP.
chroma_list_collectionsList all collection names in the Chroma database with pagination support. Args: limit: Optional maximum number of collections to return offset: Optional number of collections to skip before returning results Returns: List of collection names or ["__NO_COLLECTIONS_FOUND__"] if database is empty
chroma_create_collectionCreate a new Chroma collection with configurable HNSW parameters. Args: collection_name: Name of the collection to create embedding_function_name: Name of the embedding function to use. Options: 'default', 'cohere', 'openai', 'jina', 'voyageai', 'ollama', 'roboflow' metadata: Optional metadata dict to add to the collection
chroma_peek_collectionPeek at documents in a Chroma collection. Args: collection_name: Name of the collection to peek into limit: Number of documents to peek at
chroma_get_collection_infoGet information about a Chroma collection. Args: collection_name: Name of the collection to get info about
chroma_get_collection_countGet the number of documents in a Chroma collection. Args: collection_name: Name of the collection to count
chroma_modify_collectionModify a Chroma collection's name or metadata. Args: collection_name: Name of the collection to modify new_name: Optional new name for the collection new_metadata: Optional new metadata for the collection
Chroma - the open-source embedding database.
The fastest way to build Python or JavaScript LLM apps with memory!
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.
This is a MCP server for self-hosting your access to Chroma. If you are looking for Package Search you can find the repository for that here.
Flexible Client Types
Collection Management
Document Operations
chroma_list_collections - List all collections with pagination supportchroma_create_collection - Create a new collection with optional HNSW configurationchroma_peek_collection - View a sample of documents in a collectionchroma_get_collection_info - Get detailed information about a collectionchroma_get_collection_count - Get the number of documents in a collectionchroma_modify_collection - Update a collection's name or metadatachroma_delete_collection - Delete a collectionchroma_add_documents - Add documents with optional metadata and custom IDschroma_query_documents - Query documents using semantic search with advanced filteringchroma_get_documents - Retrieve documents by IDs or filters with paginationchroma_update_documents - Update existing documents' content, metadata, or embeddingschroma_delete_documents - Delete specific documents from a collectionChroma 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
claude_desktop_config.json file:claude_desktop_config.json file:This will create a persistent client that will use the data directory specified.
claude_desktop_config.json file: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"].
claude_desktop_config.json file:This will create an HTTP client that connects to your self-hosted Chroma instance.
Find reference usages, such as shared knowledge bases & adding memory to context windows in the Chroma MCP Docs
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.
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.
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