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turnono-datacommons-mcp-server

turnono-datacommons-mcp-server

Remote
github.com/turnonoData & Analytics0Updated 55w ago
Repository

About turnono-datacommons-mcp-server

turnono-datacommons-mcp-server exposes Data Commons API data through MCP for users seeking statistical indicators and observations. It supports HTTP and stdio transports, with data formats and chart configurations.

Key features

  1. 01Search indicators and topics
  2. 02Retrieve observations and data
  3. 03Multiple data formats
  4. 04Chart configuration support
  5. 05HTTP and stdio transports

Use cases

  1. 01Finding statistical indicators by topic
  2. 02Retrieving observations for analysis
  3. 03Connecting through HTTP or stdio

MCP support

The MCP server provides access to Data Commons API data, including tools for searching indicators and retrieving observations. It supports HTTP and stdio transports.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://server.smithery.ai/@turnono/datacommons-mcp-server/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://server.smithery.ai/@turnono/datacommons-mcp-server/mcpAuth required

Tools

Details

From the project README

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

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.