Connecting a client

The IDC MCP server is public and unauthenticated at https://api.imaging.datacommons.cancer.gov/mcp. Point any spec-conformant MCP client at that URL — there is no API key, config file, or account to set up.

Connect a client

claude.ai / Claude Desktop (remote connector)

Add the URL as a custom (remote) connector in Claude's connector settings:

https://api.imaging.datacommons.cancer.gov/mcp

Then ask, for example: "Find breast MRI in IDC, show the counts and total size, and give me a download command." The agent will discover valid filter values, size the cohort, and return the download commands on its own.

Claude Desktop / Claude Code (local, for developers)

If you are developing against the server, you can run it locally over stdio. Add to your MCP client config:

{
  "mcpServers": {
    "idc": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/IDC-REST-MCP", "idc-mcp"]
    }
  }
}

See the IDC-REST-MCP repository for install instructions.

Other MCP clients

Any spec-conformant remote-MCP client works — point it at https://api.imaging.datacommons.cancer.gov/mcp. Inspect or debug the tools with the MCP Inspector:

npx @modelcontextprotocol/inspector

How the hosted transport behaves

The hosted service uses streamable-HTTP, configured stateless with plain-JSON responses — each request is self-contained. In practice:

• Any spec-conformant remote-MCP client works, and the service autoscales behind a plain load balancer with no session affinity.

• No session handshake is needed to script it — you can POST a tools/list or tools/call directly (set Accept: application/json, text/event-stream); you don't have to initialize first or carry an Mcp-Session-Id header.

• Session-bound MCP features are not available (server→client sampling, elicitation, resource subscriptions, streamed progress) — the server exposes only client-initiated tools and static resources.

Once connected, see Tools and resources for what the server can do, and the Core concepts page for the shared data model and recommended workflow. To compare this server with the IDC agent skill, see Using IDC with an AI assistant.