---
slug: "duckdb-mcp-server"
source_type: "readme"
source_url: "https://cdn.jsdelivr.net/gh/mustafahasankhan/duckdb-mcp-server@main/README.md"
repo: "https://github.com/mustafahasankhan/duckdb-mcp-server"
source_file: "README.md"
branch: "main"
---
# DuckDB MCP Server
[](https://pypi.org/project/duckdb-mcp-server/)
[](https://pypi.org/project/duckdb-mcp-server/)
[](LICENSE)
A [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that gives AI assistants full access to [DuckDB](https://duckdb.org/) — query local files, S3 buckets, and in-memory data using plain SQL.
## 🌟 What is DuckDB MCP Server?
---
## 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 |
---
## Requirements
- Python 3.10+
- An MCP-compatible client
## Installation
```bash
pip install duckdb-mcp-server
```
**From source:**
```bash
git clone https://github.com/mustafahasankhan/duckdb-mcp-server.git
cd duckdb-mcp-server
pip install -e .
```
---
## Configuration
```
duckdb-mcp-server --db-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. |
---
## Client setup
### Claude Desktop
Edit `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows):
```json
{
"mcpServers": {
"duckdb": {
"command": "duckdb-mcp-server",
"args": ["--db-path", "~/claude-duckdb/data.db"]
}
}
}
```
### With S3 access
```json
{
"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:
```json
{
"mcpServers": {
"duckdb": {
"command": "duckdb-mcp-server",
"args": ["--db-path", "/shared/analytics.db", "--readonly"]
}
}
}
```
---
## Example conversations
### Querying a local file
> "Load sales.csv and show me the top 5 products by revenue."
```sql
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."
```sql
-- 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.
---
## 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)
---
## Development
```bash
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
```
---
## License
Contributions are welcome! Please feel free to submit a Pull Request.
[MIT](https://github.com/mustafahasankhan/duckdb-mcp-server/tree/HEAD/LICENSE)