Connect-MCP

io.github.Pak209/alphalabs-intelligence

io.github.Pak209/alphalabs-intelligence · v1.0.0

Live trading-pipeline intelligence for AI agents: signal scoring, calibration, recorded outcomes.

MCPremoteAPI keyE-commerceFinanceCRM & sales

Reported outcomes

No reports yet.

Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 6 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

B 80/100

What it does: 6 tools · 0 read, 0 write · E-commerce, Finance, CRM & sales

No tool could be tested safely. Only read-only tools that need no input are called.

How the rating is made
  • Answers without login: +30
  • Speed of first answer: +10
  • Lists tools: +10
  • Tool descriptions: +15
  • Parameter descriptions: +7
  • Read/write annotations: 0
  • Safe test call: +8
  • More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL
https://api.pak-labs.com/mcp

Tools

6 tools listed at last check
  • alphalabs_get_catalog Free: list AlphaLabs Intelligence products, prices, and auth model.
  • alphalabs_calibration_report Live paper-trading pipeline calibration telemetry: stage funnel, gate failures, near-misses. Derived analytics only — no positions, orders, or account data exist on this surface.
  • alphalabs_evaluate_signal Score YOUR trade idea through the live AlphaLabs deterministic engine: composite score, tier, per-component sub-signals, floors. Price/volume confirmation is not evaluated (no vendor market data). Returns an evaluation_id for alphalabs_explain_decision.
  • alphalabs_outcome_report Recorded outcomes of the live pipeline's own decisions: hit rates, score-band tables, accepted-vs-rejected edge, gate near-miss regret. Aggregated engine telemetry — percent moves and counts only.
  • alphalabs_feature_attribution Which engine inputs actually predict outcomes, measured on recorded live results: Spearman rankings, median-split deltas, dead inputs.
  • alphalabs_explain_decision Glass-box breakdown of a prior evaluation by evaluation_id: every sub-signal, weight, floor, and the composite reasoning.

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