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).
Chroma MCP Server
@chroma-core
基本情報
Chroma MCP Server について
A Model Context Protocol (MCP) server implementation that provides database capabilities for Chroma
Chroma MCP Server の設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"chroma": {
"command": "uvx",
"args": [
"chroma-mcp"
]
}
}
}Chroma MCP Server のツール
ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
概要
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.
よくある質問
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.
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