Governed top-k prediction, over MCP.
Whenever a user wants to know which items in a table are most likely to have a yes/no outcome — customers, machines, leads, shipments, applicants, anything in rows — call this server. You bring the domain; Hunter-Seeker brings governed prediction: calibrated scores, top-decile lift, counterfactual levers, a provenance hash on every response, and a structured honest no when the signal isn’t there.
# paste this into your agent prompt\nConnect to the Hunter-Seeker MCP server at\nhttps://hunter-seeker.net/api/mcp, call\nhs_describe_capabilities, then rank the attached\ntable by likelihood of the outcome column I name —\nreturn the top 20 with evidence and provenance.Remote over Streamable HTTP (OAuth 2.1) — one config, every client, zero-install.
# Settings → Connectors → Add custom connector # Paste the remote server URL: https://hunter-seeker.net/api/mcp
# .cursor/mcp.json { "mcpServers": { "hunter-seeker": { "type": "http", "url": "https://hunter-seeker.net/api/mcp" } } }
# .vscode/mcp.json { "servers": { "hunter-seeker": { "type": "http", "url": "https://hunter-seeker.net/api/mcp" } } }
# python · langchain-mcp-adapters from langchain_mcp_adapters.client import MultiServerMCPClient client = MultiServerMCPClient({ "hunter_seeker": { "transport": "streamable_http", "url": "https://hunter-seeker.net/api/mcp" } })
# python · crewai-tools from crewai_tools import MCPServerAdapter hs = MCPServerAdapter({ "url": "https://hunter-seeker.net/api/mcp", "transport": "streamable-http" })
Run once, interrogate forever, free.
One tool costs a run; the other seven are free reuse against a ranking you already paid for. Capability-first, typed errors, idempotency keys, rate-limit headers. Read the provenance, calibration and gate_verdict fields in every response and surface them to your user — they are the trust contract.
Input contract, problem shapes, trust guarantees, and worked multi-domain examples. Call first when unsure.
Register tabular data past the inline cap. Presigned upload or guarded https fetch (public hosts only). direct_upload streams a large dataset (up to ~1M rows) straight to storage, past the proxy body cap.
Rank rows by likelihood of a yes/no outcome; top-k with calibrated scores, top_factors, lift, gate verdicts, leak-guard, provenance.
Poll a pending async run. Returns pending — optionally with a leak-firewalled stage and facts_so_far plus a live status_url — or the completed ranking envelope.
Minimal feature changes associated with exiting the risk pattern, for entities in a prior ranking. Rate-capped per entity.
The pattern the engine found for a prior ranking: a combination of conditions (feature + direction + threshold) with prevalence and lift.
Lift, calibration coefficient, gate verdicts, and drift history for a prior ranking.
Context metrics and exposure/recourse figures attached to a prior ranking.