About Chroma MCP Server
Chroma MCP Server is an MCP server for the open-source Chroma embedding database. It lets AI models create collections, store documents and retrieve data through vector search, full-text search and metadata filtering. It suits developers building LLM apps with memory or knowledge bases, and it supports self-hosted and Chroma Cloud deployments.
Key features
- 01Ephemeral, persistent, HTTP and Cloud client types
- 02Create, modify, inspect and delete collections
- 03Add, update, delete and query documents
- 04Filtering by metadata and document content
- 05Several embedding functions with persisted configuration
Use cases
- 01Build a shared knowledge base that LLM apps can search
- 02Add long-term memory to a model's context
- 03Connect to a self-hosted or Chroma Cloud instance for vector search
MCP support
This project is an MCP server that exposes chroma_ prefixed tools to MCP clients for managing Chroma collections and documents.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
Chroma - the open-source embedding database.
The fastest way to build Python or JavaScript LLM apps with memory!
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.
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.
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 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 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
- To add an ephemeral client, add the following to your
claude_desktop_config.jsonfile:
"chroma": {
"command": "uvx",
"args": [
"chroma-mcp"
]
}
- To add a persistent client, add the following to your
claude_desktop_config.jsonfile:
"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.
- To connect to Chroma Cloud, add the following to your
claude_desktop_config.jsonfile:
"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"].
- 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.jsonfile:
"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.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
