Live trading-pipeline intelligence for AI agents: signal evaluation by a real calibrated engine with glass-box explanations, pipeline calibration telemetry, recorded outcome reports, and SEC-filing catalysts. Free beta keys + x402 USDC pay-per-call.
Scored across 6 tools
Each tool has a clearly distinct purpose: calibration telemetry, signal evaluation, explanation, feature attribution, catalog listing, and outcome reporting. No two tools overlap in functionality.
All tools share the 'alphalabs_' prefix and follow a verb_noun or noun_noun pattern. The mix of verb_noun (evaluate_signal, explain_decision, get_catalog) and noun_noun (calibration_report, feature_attribution, outcome_report) is a minor inconsistency, but still predictable and readable.
With 6 tools, the server is neither too sparse nor too heavy. Each tool covers a distinct aspect of the intelligence pipeline, making the set well-scoped for its purpose.
The tool surface covers the full lifecycle of signal evaluation: evaluation, explanation, feature analysis, calibration reporting, and outcome tracking. Missing features like market data access are explicitly out of scope, so no dead ends remain.
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.
Inputs: no named parameters.
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.
Inputs: bias (required), ticker (required), thesis, catalyst, +3 more.
Glass-box breakdown of a prior evaluation by evaluation_id: every sub-signal, weight, floor, and the composite reasoning.
Inputs: evaluation_id (required).
Which engine inputs actually predict outcomes, measured on recorded live results: Spearman rankings, median-split deltas, dead inputs.
Inputs: no named parameters.
Free: list AlphaLabs Intelligence products, prices, and auth model.
Inputs: no named parameters.
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.
Inputs: no named parameters.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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