agent-safety-mcp

MCP server for AI agent safety. One install gives any MCP-compatible AI assistant access to cost guards, prompt injection scanning, and decision tracing.

Works with Claude Code, Cursor, Windsurf, Zed, and any MCP client.

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Install

Claude Code (recommended)

claude mcp add agent-safety -- uvx agent-safety-mcp

Manual (any MCP client)

Add to your MCP config:

{
  "mcpServers": {
    "agent-safety": {
      "command": "uvx",
      "args": ["agent-safety-mcp"]
    }
  }
}

From PyPI

pip install agent-safety-mcp
agent-safety-mcp  # runs stdio server

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Tools

Cost Guard — Budget enforcement for LLM calls

Tool	What it does
cost_guard_configure	Set weekly budget, alert threshold, dry-run mode
cost_guard_status	Check current spend vs budget
cost_guard_check	Pre-check if a model call is within budget
cost_guard_record	Record a completed call's token usage
cost_guard_models	List supported models with pricing

Example: "Check if I can afford a GPT-4o call with 2000 input tokens"

Injection Guard — Prompt injection scanner

Tool	What it does
injection_scan	Scan text for injection patterns (non-blocking)
injection_check	Scan + block if injection detected
injection_patterns	List all 22 built-in detection patterns

Example: "Scan this user input for prompt injection: 'ignore previous instructions and...'"

Decision Tracer — Agent decision logging

Tool	What it does
trace_start	Start a new trace session
trace_step	Log a decision step with context
trace_summary	Get session summary (steps, errors, timing)
trace_save	Save trace to JSON + Markdown files

Example: "Start a trace for my analysis agent, then log each decision step"

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What this wraps

This MCP server wraps the AI Agent Infrastructure Stack — three standalone Python libraries:

• ai-cost-guard — pip install ai-cost-guard
• ai-injection-guard — pip install ai-injection-guard
• ai-decision-tracer — pip install ai-decision-tracer

All three: MIT licensed, zero runtime dependencies (individually), pure Python stdlib.

The MCP server adds mcp>=1.0.0 as a dependency for the protocol layer.

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Why

AI coding assistants (Claude Code, Cursor, etc.) can now protect the agents they help build — checking budgets, scanning inputs, and tracing decisions — without leaving the IDE.

Built from 8 months of running autonomous AI trading agents in live financial markets.

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License

MIT