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ai.smithery/turnono-datacommons-mcp-server

Discover statistical indicators and topics in Data Commons. Retrieve observations for specific var…

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Repository

README

From the repo.

DataCommons MCP Server

A Model Context Protocol (MCP) server for accessing Data Commons API data.

Features

  • Search for indicators and topics
  • Get observations and data
  • Support for various data formats and chart configurations
  • HTTP and stdio transport modes

Installation

Using pip

pip install -r requirements.txt
pip install -e .

Using Docker

docker build -t datacommons-mcp .
docker run -p 8000:8000 datacommons-mcp

Usage

CLI Commands

Start the server in HTTP mode:

python -m datacommons_mcp.cli serve http --host 0.0.0.0 --port 8000

Start the server in stdio mode:

python -m datacommons_mcp.cli serve stdio

Environment Variables

  • GOOGLE_API_KEY: Your Google API key for Data Commons access

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

Format code:

black .
isort .

License

MIT License

Config for your environment

Use the endpoint URL below in your config. No API key — you connect directly.

Tool

OS

Config file: ~/.cursor/mcp.json

{
  "mcpServers": {
    "mcp-server": {
      "url": "https://server.smithery.ai/@turnono/datacommons-mcp-server/mcp"
    }
  }
}

Paste into mcpServers in the config file. Restart Cursor after saving.

If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.