DuckDB MCP Server

A Model Context Protocol (MCP) server that gives AI assistants full access to DuckDB — query local files, S3 buckets, and in-memory data using plain SQL.

🌟 What is DuckDB MCP Server?

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What it does

This server exposes DuckDB to any MCP-compatible client (Claude Desktop, Cursor, VS Code, etc.). The assistant can:

• Run arbitrary SQL against local CSV, Parquet, and JSON files
• Query S3 / GCS buckets directly or cache them as local tables
• Inspect schemas, compute statistics, and suggest visualizations
• Use DuckDB's analytical SQL extensions (window functions, GROUP BY ALL, SELECT * EXCLUDE, etc.)

Tools

Tool	Description
query	Execute any SQL query. Results capped at 10 000 rows.
analyze_schema	Describe the columns and types of a file or table.
analyze_data	Row count, numeric stats (min/max/avg/median), date ranges, top categorical values.
suggest_visualizations	Suggest chart types and ready-to-run SQL queries based on column types.
create_session	Create or reset a session for cross-call context tracking.

Resources

Three built-in XML documentation resources are always available to the assistant:

Resource URI	Content
duckdb-ref://friendly-sql	DuckDB SQL extensions (GROUP BY ALL, SELECT * EXCLUDE/REPLACE, FROM-first syntax, …)
duckdb-ref://data-import	Loading CSV, Parquet, JSON, and S3/GCS data; glob patterns; multi-file reads
duckdb-ref://visualization	Chart patterns and query templates for time series, bar, scatter, and heatmap

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Requirements

• Python 3.10+
• An MCP-compatible client

Installation

pip install duckdb-mcp-server

From source:

git clone https://github.com/mustafahasankhan/duckdb-mcp-server.git
cd duckdb-mcp-server
pip install -e .

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Configuration

duckdb-mcp-server --db-path <path> [options]

Flag	Required	Description
--db-path	Yes	Path to the DuckDB file. Created automatically if it does not exist.
--readonly	No	Open the database read-only. Errors if the file does not exist.
--s3-region	No	AWS region. Defaults to AWS_DEFAULT_REGION env var, then us-east-1.
--s3-profile	No	AWS profile name. Defaults to AWS_PROFILE env var, then default.
--creds-from-env	No	Read AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY from environment.

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Client setup

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

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

With S3 access

{
  "mcpServers": {
    "duckdb": {
      "command": "duckdb-mcp-server",
      "args": [
        "--db-path", "~/claude-duckdb/data.db",
        "--s3-region", "us-east-1",
        "--creds-from-env"
      ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_KEY",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET"
      }
    }
  }
}

Read-only mode

Useful when the database is shared or managed separately:

{
  "mcpServers": {
    "duckdb": {
      "command": "duckdb-mcp-server",
      "args": ["--db-path", "/shared/analytics.db", "--readonly"]
    }
  }
}

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Example conversations

Querying a local file

"Load sales.csv and show me the top 5 products by revenue."

SELECT
    product_name,
    SUM(quantity * price) AS revenue
FROM read_csv('sales.csv')
GROUP BY ALL
ORDER BY revenue DESC
LIMIT 5;

Working with S3 data

"Cache this month's signups from S3 and show me a daily breakdown."

-- Step 1: cache the remote data locally
CREATE TABLE signups AS
  SELECT * FROM read_parquet('s3://my-bucket/signups/2026-03/*.parquet',
                             union_by_name = true);

-- Step 2: query the cached table
SELECT
    date_trunc('day', signup_at) AS day,
    COUNT(*)                     AS signups
FROM signups
GROUP BY ALL
ORDER BY day DESC;

Statistical analysis

"Give me a statistical summary of the orders table."

The assistant calls analyze_data("orders") which returns row count, numeric stats per column, date ranges, and top categorical values — no SQL required from you.

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AWS credential resolution order

1. Environment variables (--creds-from-env): AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY
2. Named profile (--s3-profile): reads ~/.aws/credentials
3. Credential chain: environment → shared credentials file → instance profile (EC2/ECS)

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Development

git clone https://github.com/mustafahasankhan/duckdb-mcp-server.git
cd duckdb-mcp-server

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

pytest

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License

Contributions are welcome! Please feel free to submit a Pull Request. MIT