iflow-mcp_datacommonsorg-datacommons-mcp

# Data Commons MCP Server This is a Model Context Protocol (MCP) server for fetching public statistical data from [Data Commons](https://datacommons.org) instances. Data Commons is an open knowledge repository that provides a unified view across multiple public data sets and statistics. This server allows any MCP-enabled agent or client to query the Data Commons knowledge graph. ## Features * **MCP-Compliant:** Implements the Model Context Protocol for seamless agent integration. * **Data Commons Access:** Fetches public statistics and data from the base datacommons.org knowledge graph. * **Custom Instance Support:** Can be configured to work with Custom Data Commons instances. * **Flexible Serving:** Runs over both streamable HTTP and stdio. ## Quickstart ### Prerequisites 1. You must have a Data Commons API key; create one at [apikeys.datacommons.org](https://apikeys.datacommons.org/). 2. Install `uv` by following the [official installation instructions](https://docs.astral.sh/uv/getting-started/installation). ### Configuration Set the following required environment variable in your shell: ``` export DC_API_KEY=<your API key> ``` ### Start the server Run the server from your command line in one of two modes: **Streamable HTTP** This runs the server with Streamable HTTP. ```bash # Runs on default port 8080 uvx datacommons-mcp serve http [--port <PORT>] ``` The server will be available at `http://localhost:<port>/mcp`. **stdio** This transport mode is intended for local integrations and is programmatically configured within a client (like Gemini CLI settings) to communicate over `stdio`. ```bash uvx datacommons-mcp serve stdio ``` ## Clients You can use any MCP-enabled agent or client to connect to your running server. For example, see the [Data Commons MCP documentation](https://github.com/datacommonsorg/agent-toolkit/blob/main/docs/user_guide.md) for guides on connecting: * [Google Gemini CLI](https://github.com/datacommonsorg/agent-toolki…