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mcp-server-duckdb

mcp-server-duckdb

by SyneHQ

mcp-server-duckdb

PyPI - Version PyPI - License

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.

Forked from https://github.com/ktanaka101/mcp-server-duckdb

Overview

This server enables interaction with a DuckDB database through the Model Context Protocol, providing a comprehensive set of tools for database operations including:

  • SQL query execution and inspection

  • Table management (creation, description, listing)

  • Data import from various sources (local files, URLs, S3)

  • Data export capabilities

  • Schema inspection and table analysis

  • Statistical summaries of table contents

The server is designed to work seamlessly with Language Models (LLMs) while maintaining data safety through optional read-only mode.

Related MCP server: mcp-server-duckdb

Components

Resources

Currently, no custom resources are implemented.

Prompts

Currently, no custom prompts are implemented.

Tools

The server implements the following database interaction tools:

  • 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)

  • show_tables: Show all tables in the DuckDB database

    • Input: No parameters required

    • Output: List of table names in the database

  • describe_table: Describe a table in the DuckDB database

    • Input: table (string) - Name of table to describe

    • Output: Table schema information

  • inspect_query: Inspect a query in the DuckDB database

    • Input: query (string) - SQL query to inspect

    • Output: Query inspection results

  • create_table_from_path: Create a table from a file path

    • Input:

      • path (string) - Path to the file to load

      • table (string, optional) - Table name to use

      • replace (boolean, optional) - Whether to replace existing table

  • create_table_from_url: Create a table from a URL

    • Input:

      • url (string) - URL to the file to load

      • table (string, optional) - Table name to use

      • replace (boolean, optional) - Whether to replace existing table

  • create_table_from_s3: Create a table from an S3 path

    • Input:

      • path (string) - S3 path to the file to load

      • table (string, optional) - Table name to use

  • create_table_from_csv: Create a table from a CSV file

    • Input:

      • path (string) - Path to the CSV file

      • table (string, optional) - Table name to use

      • delimiter (string, optional) - Delimiter to use

  • summarize_table: Get summary statistics for a table

    • Input: table (string) - Name of table to summarize

    • Output: Statistical summary of the table's contents

  • export_table_to_path: Export a table to a file

    • Input:

      • table (string) - Name of table to export

      • format (string, optional) - Format to export as (default: parquet)

      • path (string, optional) - Path to export to

  • smart_load_multiple_csv_files: Load multiple CSV files and intelligently name the tables

    • Input:

      • paths (array of strings) - List of paths to CSV files

      • delimiter (string, optional) - Delimiter to use for all CSV files

    • Output: Mapping of original table names

NOTE

While the server provides specialized functions for common operations, it also maintains the unifiedquery function for maximum flexibility. Modern LLMs can generate appropriate SQL for any database operation (SELECT, CREATE TABLE, JOIN, etc.).

NOTE

When the server is running inreadonly 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

    • 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.

Installation

Installing via Smithery

To install DuckDB Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install duckdbmcp --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

{
  "mcpServers": {
    "duckdb": {
      "command": "uvx",
      "args": [
        "duckdbmcp",
        "--db-path",
        "~/duckdbmcp/data/data.db"
      ]
    }
  }
}
  • Note: ~/duckdbmcp/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:

npx @modelcontextprotocol/inspector uv --directory ~/codes/duckdbmcp run duckdbmcp --db-path ~/duckdbmcp/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

Available Tools

12 tools
create_table_from_csvA

Create a table from a CSV file

Inputs: path (required), table, delimiter.

create_table_from_pathC

Create a table from a file path

Inputs: path (required), table, replace.

create_table_from_s3B

Create a table from an S3 path

Inputs: path (required), table.

create_table_from_urlC

Create a table from a URL

Inputs: url (required), table, replace.

describe_tableB

Describe a table in the DuckDB database

Inputs: table (required).

export_table_to_pathC

Export a table to a file

Inputs: table (required), path, format.

inspect_queryC

Inspect a query in the DuckDB database

Inputs: query (required).

is_table_existsA

Check if a table exists in the DuckDB database

Inputs: table (required).

queryC

Execute a query on the DuckDB database

Inputs: query (required).

show_tablesB

Show all tables in the DuckDB database

Inputs: include_views.

smart_load_multiple_csv_filesA

Load multiple CSV files and intelligently name the tables based on content analysis

Inputs: paths (required), delimiter.

summarize_tableC

Get summary statistics for a table

Inputs: table (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    B3.2/5.0

    Scored across 12 tools

    Disambiguation4/5

    The create_table_from_* family is distinguished by source type (path, URL, S3, CSV), though path and CSV may overlap since a CSV is a file path. query and inspect_query are distinct (execution vs inspection), and the rest target different resources.

    Naming Consistency3/5

    Most tools follow a verb_noun pattern in snake_case, but there are deviations: query is just a verb, is_table_exists uses a question-style, and smart_load_multiple_csv_files breaks the create_table_from_* convention despite similar CSV loading.

    Tool Count5/5

    Twelve tools is well within the recommended range and covers a focused set of database operations: table creation from various sources, querying, inspection, and metadata. Each tool appears purposeful.

    Completeness4/5

    The set covers loading, querying, describing, listing, and exporting tables. Missing explicit drop/alter operations, but the query tool can execute any SQL, mitigating this gap. Minor gap: no batch export or schema listing beyond describe.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

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    • A
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