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Recipe: query data via MCP

AGENTS rule 22: agents NEVER read Postgres or Iceberg directly. Every catalog / dataset / entity / pipeline read goes through a registered DataMCPTool. The bridge auto-installs every tool into the agent TOOL_REGISTRY; the same tools are reachable externally over HTTP at /mcp/data and via the alphaswarm-data-mcp stdio binary.

From inside an agent​

from alphaswarm_agents.tools import TOOL_REGISTRY

tool = TOOL_REGISTRY["data.discovery.browse"]
result = tool.invoke({"namespace_prefix": "alphaswarm_silver_yfinance"})
print(result["entries"])

From outside the platform (HTTP)​

curl -X POST http://localhost:8000/mcp/data/tools/data.discovery.browse/invoke `
    -H "Content-Type: application/json" `
    -H "Authorization: Bearer <m2m_token>" `
    -d '{"namespace_prefix":"alphaswarm_silver_yfinance"}'

From a Cursor/Continue/Cline agent (stdio)​

Register the stdio binary as an MCP server in the editor:

{
  "mcpServers": {
    "alphaswarm-data": {
      "command": "alphaswarm-data-mcp",
      "env": { "ALPHASWARM_MCP_DATA_CANONICAL_URI": "http://localhost:8000/mcp/data" }
    }
  }
}

Where to add a new tool​

Subclass DataMCPTool under alphaswarm/data/mcp/tools/, decorate with @register_data_mcp_tool, and the bridge does the rest. See Concept: data MCP.

RFC 9728 + 8707 conformance​

Every AlphaSwarm MCP server publishes Protected Resource Metadata at /.well-known/oauth-protected-resource[/...] and validates the aud claim on incoming tokens against the deployment's canonical URI. The docs site's own MCP server lives at https://docs.alpha-swarm.ai/mcp.

Deeper reads​