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mcp-server-duckdb

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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.

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.

Related MCP server: mcp-tabular

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 unifiedquery 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 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 (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:

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

{
  "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:

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

Available Tools

1 tool
queryC

Execute a query on the DuckDB database

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSQL query to execute

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states 'Execute a query', which essentially repeats the tool name. It does not reveal whether the query can mutate data, what the return format is, whether errors are handled, or any side effects. This is minimal and inadequate for a database execution tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence: 'Execute a query on the DuckDB database'. Every word earns its place, and there is no unnecessary information. It is highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has one parameter, no output schema, and no annotations. The description does not explain return values, query execution semantics, whether DDL/DML is allowed, or how it differs from related tools. Given the minimal context, the description leaves significant gaps for an agent attempting to use this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema contains one parameter with full 100% description coverage: 'SQL query to execute'. The description adds no additional meaning beyond the schema, so the baseline of 3 applies. The schema already clearly documents the parameter's purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action: 'Execute a query on the DuckDB database'. It clearly identifies the resource (DuckDB) and the operation (execute a query). However, it does not distinguish itself from the sibling tool 'inspect_query', which also involves queries, so it misses the differentiation needed for a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives such as inspect_query or the table creation tools. The description gives no context about appropriate use cases, prerequisites, or exclusions, leaving the agent without decision support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

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

  1. 1 tool updatev1.1.0
    • First observedquery
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion between tools. The 'query' tool has a single, unambiguous purpose of executing DuckDB queries.

Naming Consistency5/5

The single tool name 'query' is a clear, straightforward verb that accurately describes its function. With only one tool, naming consistency is trivially satisfied.

Tool Count3/5

One tool is on the thin side for a full database server like DuckDB. While a generic query tool can cover many operations, it lacks typical helper tools (e.g., schema listing, table inspection) that agents might expect, making the count borderline.

Completeness4/5

The query tool can execute any SQL, enabling full CRUD and DDL operations, so there are no direct dead ends. However, there is no built-in schema discovery or validation, which is a minor gap that agents can work around via SQL queries against system tables.

Resources

Related MCP Connectors

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