CDM MCP Server
Enables AI assistants to interact with Delta Lake tables stored in MinIO through Spark using natural language queries. Provides read-oriented data operations on Delta Lake tables through the Model Context Protocol.
README
BERDL Datalake MCP Server
A FastAPI-based service that enables AI assistants to interact with Delta Lake tables stored in MinIO through Spark, implementing the Model Context Protocol (MCP) for natural language data operations.
⚠️ Important Warning:
This service allows arbitrary
read-orientedqueries to be executed against Delta Lake tables. Query results will be sent to the model host server, unless you are hosting your model locally.
❌ Additionally, this service is NOT approved for deployment to any production environment, including CI, until explicit approval is granted by KBase leadership. Use strictly for local development or evaluation purposes only.
Documentation
For detailed documentation, please refer to the User Guide. The guide covers:
- Quick Start - Bring the local service up and running
- Creating Sample Delta Tables - Set up local test data
- Using the API - Direct API usage examples
- AI Assistant Integration - Configure and use with MCP Host tools
- MCP Configuration - Create
mcp.json - MCP Host Setup - Configure MCP Host
- Example Prompts - Natural language examples
- MCP Configuration - Create
Quick Start
-
Clone the repository:
git clone https://github.com/BERDataLakehouse/datalake-mcp-server.git cd datalake-mcp-server -
Create required directories:
mkdir -p cdr/berdl/jupyter/berdl_shared_workspace -
Create Docker network:
docker network create berdl-jupyterhub-network -
Start the services:
docker-compose up -d --build -
Access the services:
- MCP Server API Docs: http://localhost:8000/apis/mcp/docs
- MCP Server (root): http://localhost:8000/docs (no operations - use /apis/mcp/docs)
- MinIO Console: http://localhost:9003
- Spark Master UI: http://localhost:8090
- JupyterHub: http://localhost:4043
Note: The MCP server is mounted at
/apis/mcpby default. SetSERVICE_ROOT_PATH=""environment variable to serve at root.
Testing
# Install dependencies (only required on first run or when the uv.lock file changes)
uv sync --locked
# Run tests
PYTHONPATH=. uv run pytest tests
# Run with coverage
PYTHONPATH=. uv run pytest --cov=src tests/
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
E2B
Using MCP to run code via e2b.