What is MotherDuck MCP Server?
The MotherDuck MCP Server integrates MotherDuck a cloud-native version of DuckDB with intelligent agents through the Model Context Protocol (MCP). This server enables LLM based agents to perform SQL queries, analyze structured data, and retrieve insights from DuckDB databases hosted on MotherDuck.
With a simple setup, agents can use natural language to interact with real data, making it easier to build analytics copilots, data QA bots, and AI-driven dashboards that operate directly on production-grade SQL engines.
Key Features of MotherDuck MCP Server
- Connect agents to MotherDuck’s managed DuckDB environment for fast, scalable, and efficient SQL execution.
- Retrieve tables, columns, schema metadata, and execute analytical queries through natural language interfaces.
- Designed to integrate with any MCP compatible LLM framework such as OpenAI Agent SDK, LangChain, CrewAI, or Vercel’s AI SDK.
- Minimal setup with support for secure token based access to your MotherDuck account and databases.
- Ideal for RAG pipelines, business intelligence agents, and data-aware copilots.
Installation
- Clone the Repository
git clone https://github.com/motherduckdb/mcp-server-motherduck cd mcp-server-motherduck
- Install Requirements
Ensure Python and required dependencies are installed. - Configure Credentials
Set your MotherDuck token and database connection info in the environment or config file. - Run the Server
Start the MCP server to allow agents to begin querying via the protocol.
MotherDuck MCP Server Use Cases
- Data Analysis Assistants
Let LLM agents perform SQL queries on real datasets for dashboards, reports, or insight generation. - Natural Language Interfaces for SQL
Build chat based data explorers where users can ask questions and receive results from DuckDB-powered analytics. - Retrieval Augmented Generation (RAG)
Pull structured data from MotherDuck as part of a broader agent driven RAG pipeline. - Developer Copilots for Data Teams
Assist data scientists or engineers by auto-generating and validating queries against live datasets.
