MCP Server

alphalabs-intelligence

io.github.Pak209/alphalabs-intelligence
Data & Analytics Finance & Investing Public & reachable MCP 2026-07-28

What this MCP does

Scores user-provided trade ideas and reports calibration, feature attribution, and recorded outcomes from a paper-trading signal pipeline.

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.
Input schema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
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.
Input schema
{'type': 'object', 'required': ['ticker', 'bias'], 'properties': {'bias': {'enum': ['bullish', 'bearish', 'neutral'], 'type': 'string'}, 'thesis': {'type': 'string', 'description': 'Why it should move the stock'}, 'ticker': {'type': 'string', 'description': 'Symbol, e.g. NVDA'}, 'catalyst': {'type': 'string', 'description': 'What just happened (headline/event)'}, 'confidence': {'type': 'number', 'maximum': 1, 'minimum': 0, 'description': 'Your own conviction 0-1 (echoed, not scored)'}, 'catalyst_type': {'type': 'string', 'description': "Optional label, e.g. 'Government Contract'"}, 'catalyst_score': {'type': 'number', 'maximum': 100, 'minimum': 0, 'description': 'Optional 0-100 materiality if you scored it'}}, 'additionalProperties': False}
alphalabs_explain_decision
Glass-box breakdown of a prior evaluation by evaluation_id: every sub-signal, weight, floor, and the composite reasoning.
Input schema
{'type': 'object', 'required': ['evaluation_id'], 'properties': {'evaluation_id': {'type': 'string'}}, 'additionalProperties': False}
alphalabs_feature_attribution
Which engine inputs actually predict outcomes, measured on recorded live results: Spearman rankings, median-split deltas, dead inputs.
Input schema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
alphalabs_get_catalog
Free: list AlphaLabs Intelligence products, prices, and auth model.
Input schema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
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.
Input schema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Added
alphalabs_explain_decision
Sept. 17, 2026, 12:45 p.m.
Added
alphalabs_feature_attribution
Sept. 17, 2026, 12:45 p.m.
Added
alphalabs_outcome_report
Sept. 17, 2026, 12:45 p.m.
Added
alphalabs_evaluate_signal
Sept. 17, 2026, 12:45 p.m.
Added
alphalabs_calibration_report
Sept. 17, 2026, 12:45 p.m.
Added
alphalabs_get_catalog
Sept. 17, 2026, 12:45 p.m.