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MCP server for AI agent safety — cost guards, injection scanning, and decision tracing

Project description

agent-safety-mcp

PyPI version License: MIT Python 3.10+

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.


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

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"


What this wraps

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

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.


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.


License

MIT

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