alphalabs-intelligence
Live trading-pipeline intelligence for AI agents: signal scoring, calibration, recorded outcomes.
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (1)
- LOWTool 'alphalabs_explain_decision' description lacks action verbin alphalabs_explain_decision
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"alphalabs-intelligence": {
"url": "https://api.pak-labs.com/mcp"
}
}
}Remote-Endpunkte
https://api.pak-labs.com/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (6)
🟢alphalabs_get_catalog
Free: list AlphaLabs Intelligence products, prices, and auth model.
Eingabe-Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}⚪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.
Eingabe-Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟢alphalabs_evaluate_signal(ticker, bias, confidence, catalyst, thesis, ...)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"ticker": {
"type": "string",
"description": "Symbol, e.g. NVDA"
},
"bias": {
"type": "string",
"enum": [
"bullish",
"bearish",
"neutral"
]
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1,
"description": "Your own conviction 0-1 (echoed, not scored)"
},
"catalyst": {
"type": "string",
"description": "What just happened (headline/event)"
},
"thesis": {
"type": "string",
"description": "Why it should move the stock"
},
"catalyst_type": {
"type": "string",
"description": "Optional label, e.g. 'Government Contract'"
},
"catalyst_score": {
"type": "number",
"minimum": 0,
"maximum": 100,
"description": "Optional 0-100 materiality if you scored it"
}
},
"required": [
"ticker",
"bias"
],
"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.
Eingabe-Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}⚪alphalabs_feature_attribution
Which engine inputs actually predict outcomes, measured on recorded live results: Spearman rankings, median-split deltas, dead inputs.
Eingabe-Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟢alphalabs_explain_decision(evaluation_id)
Glass-box breakdown of a prior evaluation by evaluation_id: every sub-signal, weight, floor, and the composite reasoning.
Eingabe-Schema
{
"type": "object",
"properties": {
"evaluation_id": {
"type": "string"
}
},
"required": [
"evaluation_id"
],
"additionalProperties": false
}Community
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