MCP server
Prop Firm Compliance API — MCP server setup for AI agents
Configure the SEIGYO MCP server so AI agents can evaluate trades, check session health, and enforce discipline rules as callable tools.
Who this page is for
Prop firm operators and risk managers who need programmatic enforcement of challenge rules across a fleet of trader accounts.
Core problem
Manually monitoring trader compliance does not scale. By the time a risk manager sees the violation, the account has already breached.
Why this matters
Why this page exists
The page should answer the exact query before asking the user to convert.
MCP is the standard protocol for AI agents to call external tools. SEIGYO's MCP server exposes discipline as a tool.
Any MCP-compatible agent (Claude, GPT, custom LLMs) can call evaluate_trade before executing an order.
The agent gets human-readable explanations of why a trade was blocked, not just a boolean.
What to do first
Start with the smallest useful workflow
A specific first step keeps the page practical and reduces decision fatigue.
Add the SEIGYO MCP server to your agent's tool configuration with your API key.
Give the agent access to evaluate_trade, check_session, get_rule_types, and interpret_verdict.
Let the agent use check_session proactively to assess whether it should keep trading.
What to measure
Look for signals that change behavior
Useful review starts with a small number of repeatable measurements.
fleet-wide compliance rate, daily loss breaches prevented, and time to enforcement
Agent tool call frequency and verdict distribution
Whether the agent respects blocked verdicts without override attempts
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.
Four tools cover the full discipline loop: evaluate, check, discover rules, and interpret results.
The MCP server runs locally via stdio — no additional network infrastructure required.
Works with Claude Desktop, Claude Code, and any MCP-compatible AI framework.