MCP server

AI Agent Discipline — 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.

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Who this page is for

Teams building LLM-powered trading agents (Claude, GPT, custom models) who need behavioral guardrails between the model and the market.

Core problem

AI agents can reason about strategy but cannot feel risk. Without external guardrails, an agent will keep trading through tilt conditions that any human would recognize.

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.

blocked trades per session, agent compliance rate, and behavioral pattern alerts

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.

AI Agent Discipline — MCP server setup for AI agents

Why this page exists

Start with the smallest useful workflow

Look for signals that change behavior

Where SEIGYO fits

Open the next specific page