The Data Commons Model Context Protocol (MCP) service gives AI agents access to the Data Commons knowledge graph and returns data related to statistical variables, topics, and observations. It allows end users to formulate complex natural-language queries interactively, get data in textual, structured or unstructured formats, and download the data as desired. For example, depending on the agent, a user can answer high-level questions such as “give me the economic indicators of the BRICS countries”, view simple tables, and download a CSV file of the data in tabular format.

The MCP server returns data from datacommons.org by default or can be configured to query a Custom Data Commons instance.

The server is a Python binary based on the FastMCP 2.0 framework. A prebuilt package is available at https://pypi.org/project/datacommons-mcp/.

At this time, there is no centrally deployed server; you run your own server, and any client you want to connect to it.

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Tools

The server currently supports the following tools:

search_indicators: Searches for available variables and/or topics (a hierarchy of sub-topics and member variables) for a given place or metric.
get_observations: Fetches statistical data for a given variable and place.

Clients

To connect to the Data Commons MCP Server, you can use any available AI application that supports MCP, or your own custom agent.

The server supports both standard MCP transport protocols:

Stdio: For clients that connect directly using local processes
Streamable HTTP: For clients that connect remotely or otherwise require HTTP (e.g. Typescript)

See Run MCP tools for procedures for using Gemini CLI and the Gemini CLI Data Commons Extension.

Unsupported features

At the current time, the following are not supported:

Non-geographical (“custom”) entities
Events
Exploring nodes and relationships in the graph
Returning data formatted for graphic visualizations

Disclaimer

AI applications using the MCP server can make mistakes, so please double-check responses.

Page last updated: November 11, 2025 • Send feedback about this page