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DuckDB MCP Server

MCP Server

Local SQL engine for LLM-powered data queries

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Updated Dec 25, 2024

About

A Model Context Protocol server that exposes DuckDB database operations—querying, table creation, and schema inspection—to language models. It supports read‑only mode to protect data integrity.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

mcp-server-duckdb MCP server

The Ktanaka101 Mcp Server Duckdb is a lightweight, MCP‑compatible gateway that lets AI assistants like Claude interact with a local DuckDB database. By exposing standard SQL operations through MCP tools, it removes the need for developers to write custom connectors or manage database drivers manually. The server shines in scenarios where quick, on‑premise data exploration is required—such as analytics demos, rapid prototyping, or private data research—because DuckDB’s in‑process engine delivers high performance without a separate database server.

At its core, the server implements five intuitive tools that cover most everyday data tasks. read‑query allows LLMs to retrieve information with statements, while write‑query handles updates, inserts, and deletes. The create-table tool supports schema evolution on the fly, and list-tables + describe‑table give instant visibility into the database structure. A read‑only mode is available to lock the database against accidental writes, making it safe for environments where data integrity must be preserved. This mode automatically disables the write‑capable tools, ensuring that the LLM can only perform safe read operations.

Developers benefit from a plug‑and‑play integration: the server accepts a simple argument to point at any DuckDB file, creating it if necessary. When run with , the database is opened in a read‑only connection, adding an extra layer of security. Because MCP handles the communication protocol, any AI assistant that supports MCP can immediately start sending SQL commands without additional configuration.

Typical use cases include:

  • Data analysis pipelines where an LLM generates exploratory queries and visualizes results.
  • Chatbot back‑ends that need quick access to structured knowledge bases stored locally.
  • Educational tools for teaching SQL by letting students ask an AI to perform queries on a sandbox database.
  • Rapid prototyping of data‑driven features in new applications, allowing developers to test logic against a real database without deploying a full stack.

The server’s standout advantage lies in its minimal footprint and zero‑dependency requirement beyond DuckDB. It removes the friction of setting up a database server, lets developers focus on business logic, and ensures that AI assistants can safely read from or write to a local database with minimal risk.