Chroma MCP Server

Name: Chroma MCP Server
Author: chroma-core

@chroma-core

代码仓库

关于 Chroma MCP Server

A Model Context Protocol (MCP) server implementation that provides database capabilities for Chroma

基本信息

分类

数据库

许可证

Apache-2.0

运行时

python

传输方式

stdio

发布者

chroma-core

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Chroma MCP Server?

Chroma MCP Server is an MCP server that provides data retrieval capabilities powered by Chroma, the open-source embedding database. It enables AI models to create collections over generated data and user inputs, and retrieve data using vector search, full text search, metadata filtering, and more. It is designed for developers building LLM apps that need memory and context.

How to use Chroma MCP Server?

The server can be used with Claude Desktop by adding it to claude_desktop_config.json using uvx with arguments specifying the client type (ephemeral, persistent, HTTP for self-hosted, or cloud). Configuration can also be done via environment variables. Example: {"chroma": {"command": "uvx", "args": ["chroma-mcp", "--client-type", "persistent", "--data-dir", "/path/to/data"]}}.

Key features of Chroma MCP Server

Flexible client types: ephemeral, persistent, HTTP, and cloud.
Create, modify, delete collections with optional HNSW configuration.
Add documents with metadata and custom IDs.
Query documents using semantic search with advanced filtering.
Full text search and metadata filtering capabilities.
Supports multiple embedding functions (default, cohere, openai, jina, voyageai, roboflow).

Use cases of Chroma MCP Server

Adding memory to LLM context windows.
Building shared knowledge bases for AI assistants.
Performing semantic search over document collections.
Managing and querying vector embeddings with metadata filters.

FAQ from Chroma MCP Server

What client types are supported?

The server supports ephemeral (in-memory for testing), persistent (file-based storage), HTTP (for self-hosted Chroma instances), and cloud (for Chroma Cloud, automatically connecting to api.trychroma.com).

How do I configure embedding functions?

Embedding functions are selected when creating a collection. Supported functions: default, cohere, openai, jina, voyageai, roboflow. For external APIs, set the environment variable CHROMA_<API>_API_KEY (e.g., CHROMA_COHERE_API_KEY). Embedding function persistence is supported from Chroma v1.0.0 onward; collections created with ≤0.6.3 do not persist the function.

How do I connect to Chroma Cloud?

Use --client-type cloud along with --tenant, --database, and --api-key arguments in the server configuration. Alternatively, set CHROMA_TENANT, CHROMA_DATABASE, and CHROMA_API_KEY environment variables. The cloud client automatically connects via SSL to api.trychroma.com.

What environment variables can I use?

Common variables: CHROMA_CLIENT_TYPE, CHROMA_DATA_DIR (persistent), CHROMA_TENANT, CHROMA_DATABASE, CHROMA_API_KEY (cloud), CHROMA_HOST, CHROMA_PORT, CHROMA_CUSTOM_AUTH_CREDENTIALS, CHROMA_SSL (HTTP). The server loads from a .env file (default .chroma_env) or system environment variables. Command-line arguments take precedence.

Is there a limit on data storage or collection size?

The README does not specify explicit limits. Persistent and cloud clients store data according to their backend (file system or Chroma Cloud), while ephemeral is in-memory and lost on restart.