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A Model Context Protocol (MCP) server implementation for DuckDB, providing database interaction capabilities

Publisherktanaka101
Repositorymcp-server-duckdb
LanguagePython
Forks
21
Stars
174
Available tools
0
Transport typestdio
Categories
LicenseMIT
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  • Connect tools to AI workflows

    DuckDB exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    174 stars and 21 forks from the linked repository.

mcp-server-duckdb

PyPI - Version PyPI - License smithery badge

A Model Context Protocol (MCP) server implementation for DuckDB, providing database interaction capabilities through MCP tools. It would be interesting to have LLM analyze it. DuckDB is suitable for local analysis.

Overview

This server enables interaction with a DuckDB database through the Model Context Protocol, allowing for database operations like querying, table creation, and schema inspection.

Components

Resources

Currently, no custom resources are implemented.

Prompts

Currently, no custom prompts are implemented.

Tools

The server implements the following database interaction tool:

  • query: Execute any SQL query on the DuckDB database
    • Input: query (string) - Any valid DuckDB SQL statement
    • Output: Query results as text (or success message for operations like CREATE/INSERT)

[!NOTE] The server provides a single unified query function rather than separate specialized functions, as modern LLMs can generate appropriate SQL for any database operation (SELECT, CREATE TABLE, JOIN, etc.) without requiring separate endpoints.

[!NOTE] When the server is running in readonly mode, DuckDB's native readonly protection is enforced. This ensures that the Language Model (LLM) cannot perform any write operations (CREATE, INSERT, UPDATE, DELETE), maintaining data integrity and preventing unintended changes.

Configuration

Required Parameters

  • db-path (string): Path to the DuckDB database file
    • The server will automatically create the database file and parent directories if they don't exist
    • If --readonly is specified and the database file doesn't exist, the server will fail to start with an error

Optional Parameters

  • --readonly: Run server in read-only mode (default: false)
    • Description: When this flag is set, the server operates in read-only mode. This means:
      • The DuckDB database will be opened with read_only=True, preventing any write operations.
      • If the specified database file does not exist, it will not be created.
      • Security Benefit: Prevents the Language Model (LLM) from performing any write operations, ensuring that the database remains unaltered.
    • Reference: For more details on read-only connections in DuckDB, see the DuckDB Python API documentation.
  • --keep-connection: Re-uses a single DuckDB connection mode (default: false)
    • Description: When this flag is set, Re-uses a single DuckDB connection for the entire server lifetime. Enables TEMP objects & slightly faster queries, but can hold an exclusive lock on the file.

Installation

Installing via Smithery

To install DuckDB Server for Claude Desktop automatically via Smithery:

bash
npx -y @smithery/cli install mcp-server-duckdb --client claude

Claude Desktop Integration

Configure the MCP server in Claude Desktop's configuration file:

MacOS

Location: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows

Location: %APPDATA%/Claude/claude_desktop_config.json

json
{
  "mcpServers": {
    "duckdb": {
      "command": "uvx",
      "args": [
        "mcp-server-duckdb",
        "--db-path",
        "~/mcp-server-duckdb/data/data.db"
      ]
    }
  }
}
  • Note: ~/mcp-server-duckdb/data/data.db should be replaced with the actual path to the DuckDB database file.

Development

Prerequisites

  • Python with uv package manager
  • DuckDB Python package
  • MCP server dependencies

Debugging

Debugging MCP servers can be challenging due to their stdio-based communication. We recommend using the MCP Inspector for the best debugging experience.

Using MCP Inspector

  1. Install the inspector using npm:
bash
npx @modelcontextprotocol/inspector uv --directory ~/codes/mcp-server-duckdb run mcp-server-duckdb --db-path ~/mcp-server-duckdb/data/data.db
  1. Open the provided URL in your browser to access the debugging interface

The inspector provides visibility into:

  • Request/response communication
  • Tool execution
  • Server state
  • Error messages

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "duckdb": {
      "command": "uvx",
      "args": [
        "mcp-server-duckdb",
        "--db-path",
        "~/mcp-server-duckdb/data/data.db"
      ]
    }
  }
}

Use DuckDB MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once DuckDB is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add DuckDB as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the DuckDB MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "duckdb": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-server-duckdb"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the DuckDB MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the DuckDB MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use DuckDB to help me with this task?
DuckDB
Sure. I read it.
Here is what I found using DuckDB.

Frequently asked questions

What is the DuckDB MCP server used for?

DuckDB is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use DuckDB MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use DuckDB with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use DuckDB MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With DuckDB connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect DuckDB MCP to TypingMind?

DuckDB runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does DuckDB MCP provide in TypingMind?

DuckDB exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use DuckDB MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If DuckDB requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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