{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_s9svu4c3rkh5","handle":"iflow-mcp-datacommonsorg-datacommons-mcp","url":"https://wellknown.network/agents/iflow-mcp-datacommonsorg-datacommons-mcp","links":{"self":"https://wellknown.network/agents/iflow-mcp-datacommonsorg-datacommons-mcp/record.json","html":"https://wellknown.network/agents/iflow-mcp-datacommonsorg-datacommons-mcp","markdown":"https://wellknown.network/agents/iflow-mcp-datacommonsorg-datacommons-mcp/record.md","api":"https://wellknown.network/api/v1/agents/iflow-mcp-datacommonsorg-datacommons-mcp","status":"https://wellknown.network/api/v1/agents/iflow-mcp-datacommonsorg-datacommons-mcp/status","claim":"https://wellknown.network/agents/iflow-mcp-datacommonsorg-datacommons-mcp/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/iflow-mcp-datacommonsorg-datacommons-mcp/claim.json","badge":"https://wellknown.network/agents/iflow-mcp-datacommonsorg-datacommons-mcp/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/iflow-mcp-datacommonsorg-datacommons-mcp/history","tools":"https://wellknown.network/api/v1/agents/iflow-mcp-datacommonsorg-datacommons-mcp/tools"},"ard":{"identifier":"urn:air::server:iflow-mcp-datacommonsorg-datacommons-mcp","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"iflow-mcp_datacommonsorg-datacommons-mcp","summary":"Data Commons MCP server.","description":"# Data Commons MCP Server\n\nThis is a Model Context Protocol (MCP) server for fetching public statistical data from [Data Commons](https://datacommons.org) instances.\n\nData 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.\n\n## Features\n* **MCP-Compliant:** Implements the Model Context Protocol for seamless agent integration.\n* **Data Commons Access:** Fetches public statistics and data from the base datacommons.org knowledge graph.\n* **Custom Instance Support:** Can be configured to work with Custom Data Commons instances.\n* **Flexible Serving:** Runs over both streamable HTTP and stdio.\n\n## Quickstart\n\n### Prerequisites\n\n1.  You must have a Data Commons API key; create one at [apikeys.datacommons.org](https://apikeys.datacommons.org/).\n2.  Install `uv` by following the [official installation instructions](https://docs.astral.sh/uv/getting-started/installation).\n\n### Configuration\n\nSet the following required environment variable in your shell:\n\n```\nexport DC_API_KEY=<your API key>\n```\n\n### Start the server \n\nRun the server from your command line in one of two modes:\n\n**Streamable HTTP**\n\nThis runs the server with Streamable HTTP.\n\n```bash\n# Runs on default port 8080\nuvx datacommons-mcp serve http [--port <PORT>]\n```\n\nThe server will be available at `http://localhost:<port>/mcp`.\n\n**stdio**\n\nThis transport mode is intended for local integrations and is programmatically configured within a client (like Gemini CLI settings) to communicate over `stdio`.\n\n```bash\nuvx datacommons-mcp serve stdio\n```\n\n## Clients\n\nYou 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:\n* [Google Gemini CLI](https://github.com/datacommonsorg/agent-toolki…","publisher":null,"homepage":"https://github.com/datacommonsorg/agent-toolkit","repository":"https://github.com/datacommonsorg/agent-toolkit","version":"1.1.6","license":"Apache License Version 2.0, January 2004 http://www.apache.org/…","protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_datacommonsorg-datacommons-mcp","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","license":"pypi","repoUrl":"pypi","summary":"pypi","version":"pypi","description":"pypi","homepageUrl":"pypi"}},"derived":{"capabilities":[{"slug":"dev.terminal","name":"Terminal & Shell","confidence":1,"provenance":"derived"},{"slug":"knowledge.knowledge-graph","name":"Knowledge Graphs","confidence":0.836,"provenance":"derived"},{"slug":"dev.version-control","name":"Version Control","confidence":0.825,"provenance":"derived"}],"categories":["dev","knowledge"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_datacommonsorg-datacommons-mcp","registry":"pypi","observedAt":"2026-10-28T04:28:15.672Z","publishedAt":"2026-02-07T23:47:42.335797Z","latestVersion":"1.1.6"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_datacommonsorg-datacommons-mcp","url":"https://pypi.org/project/iflow-mcp_datacommonsorg-datacommons-mcp/","firstSeenAt":"2026-09-09T18:21:42.921Z","fetchedAt":"2026-10-28T04:26:17.720Z","normalizedAt":"2026-10-28T04:26:17.720Z"}]},"firstSeenAt":"2026-09-09T18:21:42.921Z","updatedAt":"2026-10-28T04:28:15.672Z"}