Behavioral detection
MCP Trading Tools — behavioral pattern detection via API
Use SEIGYO's API to detect revenge trading, tilt, overconfidence, fatigue, and 4 other behavioral patterns in mcp trading tools.
Who this page is for
AI developers using Model Context Protocol to build trading agents that need real-time discipline evaluation as a callable tool.
Core problem
MCP-compatible agents need tools they can call to evaluate trades. Without a discipline tool, the agent has no way to check whether a trade violates behavioral rules.
Why this matters
Why this page exists
The page should answer the exact query before asking the user to convert.
Rule violations are the symptom. Behavioral patterns are the cause. The API detects both.
Seven patterns — revenge trading, frequency spike, size escalation, overconfidence, fatigue, time vulnerability, streak degradation — are checked on every evaluation.
Pattern alerts include severity, confidence, and recommended action so your system can respond proportionally.
What to do first
Start with the smallest useful workflow
A specific first step keeps the page practical and reduces decision fatigue.
Include session state (trade count, cumulative P&L, consecutive losses, timestamps) in every API call.
Check the patterns array in the evaluation response alongside the verdict.
Use pattern alerts to trigger escalation, notifications, or position size reduction.
What to measure
Look for signals that change behavior
Useful review starts with a small number of repeatable measurements.
tool call latency, blocked trades per session, and agent decision quality after warnings
Pattern alert frequency by type and severity
Correlation between pattern alerts and subsequent P&L degradation
How it helps
Where SEIGYO fits
Move from the query into a workflow users can try with demo data, CSV history, or a setup path.
Detection happens inline with trade evaluation — no separate analytics pipeline required.
Pattern alerts feed into the enforcement state machine, escalating from warn to pause to lock.
Works for fleet-wide monitoring: aggregate pattern frequency across all traders in one dashboard.