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Data Commons MCP Server

Data Commons MCP Server

by Data Commons
Released
v1.4.0
Apache-2.0
Repository Docs
Databases & Data

Summary

Query public statistics — health, economic, demographic and climate indicators from hundreds of official sources — through the Data Commons knowledge graph, hosted or self-run.

Features

  • search_indicators — find the right statistical variable from a natural-language description
  • search_child_indicators — walk down a topic hierarchy to narrower indicators
  • get_variable_metadata — what a variable measures, its units and periodicity
  • get_observations — values for a given variable and place
  • get_child_observations — the same variable across every child place (all counties in a state)
  • get_multi_entity_observations — compare several entities side by side
  • Hosted endpoint at api.datacommons.org/mcp with a free API key — nothing to deploy
  • Self-host over streamable HTTP or stdio against a Custom Data Commons instance

Installation

Set up this MCP server in your favorite AI agent — copy a ready-made configuration below.

Any MCP-compatible agent

Most agents (Claude, Cursor, Windsurf, VS Code, and more) read a standard mcpServers configuration.

  1. Open your agent's MCP configuration file.
  2. Merge the snippet below into it, filling in the environment variables with your own values.
  3. Restart the agent — the "Data Commons MCP Server" tools will be available.
{
  "mcpServers": {
    "data-commons-mcp": {
      "command": "uvx",
      "args": [
        "datacommons-mcp",
        "serve",
        "stdio"
      ],
      "env": {
        "DC_API_KEY": "YOUR_DATA_COMMONS_API_KEY"
      }
    }
  }
}

Description

Data Commons is Google's open knowledge graph that normalises public statistics from hundreds of official sources — census bureaus, health ministries, the World Bank, the UN system and more — into one schema where every indicator shares a variable ID and every place shares an entity ID. The MCP server puts that graph behind six tools, so an agent can answer a statistical question by retrieving numbers rather than recalling them.

The tools split discovery from retrieval, which is what makes the graph tractable for a model that cannot see it all at once. search_indicators and search_child_indicators find the right statistical variable from natural language and walk down a topic hierarchy; get_variable_metadata explains what a variable actually measures, in which units, at which periodicity; then get_observations, get_child_observations and get_multi_entity_observations fetch the values — for one place, for every child of a place (every county in a state, say), or for several entities compared side by side.

The framing worth keeping in mind: this is a citation layer as much as a data layer. Asking a model for a country's under-five mortality rate gets you a plausible number; asking Data Commons gets you a number attached to a named source and a reference period.

Running it. The hosted service at https://api.datacommons.org/mcp needs no deployment — just a free Data Commons API key from apikeys.datacommons.org, sent as an X-API-Key header. For local work or an organisation's own Custom Data Commons instance, the datacommons-mcp PyPI package runs under uvx in streamable-HTTP mode (uvx datacommons-mcp serve http) or stdio mode (uvx datacommons-mcp serve stdio), with DC_API_KEY in the environment. Note that the hosted endpoint serves base datacommons.org only; a Custom Data Commons deployment must run its own server.

Apache-2.0. Version 1.4.0 shipped 3 September 2026, and the same MCP interface now fronts the UN System Data Commons.

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