CDM MCP Server

Name: CDM MCP Server
Author: kbase

@kbase

リポジトリ

CDM MCP Server について

概要はまだありません

基本情報

カテゴリ

その他

ライセンス

MIT

ランタイム

python

トランスポート

stdio

公開者

kbase

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": {
    "cdm-mcp-server": {
      "command": "docker",
      "args": [
        "network",
        "create",
        "cdm-jupyterhub-network"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is CDM MCP Server?

A FastAPI-based service that implements the Model Context Protocol (MCP) to enable AI assistants to interact with Delta Lake tables stored in MinIO through Spark. It is intended for local development or evaluation only and is not approved for production deployment.

How to use CDM MCP Server?

Clone the repository, create required directories, set up a Docker network, and start the services with docker-compose up -d --build. Then access the MCP server at http://localhost:8000/docs. For AI assistant integration, configure an mcp.json file and set up an MCP host.

Key features of CDM MCP Server

Read-oriented queries against Delta Lake tables via natural language
Integration with MinIO object storage and Spark
Implements the Model Context Protocol for AI assistants
Quick local setup using Docker Compose
Includes sample data creation and API usage examples
Supports JupyterHub and Spark Master UI

Use cases of CDM MCP Server

AI assistants querying Delta Lake tables stored in MinIO
Local development and evaluation of MCP-based data interactions
Prototyping natural language data operations with Spark

FAQ from CDM MCP Server

What does CDM MCP Server do?

It allows AI assistants to execute read-oriented queries against Delta Lake tables stored in MinIO through Spark, following the Model Context Protocol.

Is CDM MCP Server approved for production?

No. The README explicitly states it is not approved for deployment to any production environment, including CI, without explicit approval from KBase leadership.

What are the runtime requirements?

Docker, Docker Compose, and a local network (cdm-jupyterhub-network). The service runs locally via docker-compose up.

Where does data live?

Data is stored in MinIO (object storage) and accessed via Spark. The service creates a local shared workspace directory (cdr/cdm/jupyter/cdm_shared_workspace).