debugpy_step_out · Debugpy MCP: Risk & Policy

WHAT IT DOES

What debugpy_step_out does on Debugpy

AI agents invoke debugpy_step_out to trigger actions in Debugpy. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why debugpy_step_out needs a policy

This tool triggers a debugger action that controls execution flow of a running Python process inside a Docker container. 'stepOut' resumes execution until the current function returns, which is an active execution control operation. While not destructive on its own, misuse could cause the debugged process to advance past critical breakpoints or execute unintended code paths.

From the tool's definition Step out of the current function (DAP 'stepOut')

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

debugpy_step_out Rate-limited

debugpy_step_out stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "debugpy_step_out": {
      "limits": [
        {
          "counter": "debugpy_step_out_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Debugpy, so you can run agents against it safely.

FAQ

Questions about debugpy_step_out

What does the debugpy_step_out tool do? +

Step out of the current function (DAP 'stepOut'). It is categorised as a Execute tool in the Debugpy MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on debugpy_step_out? +

Register the Debugpy MCP server in PolicyLayer and add a rule for debugpy_step_out: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Debugpy. Nothing to install.

What risk level is debugpy_step_out? +

debugpy_step_out is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit debugpy_step_out? +

Yes. Add a rate_limit block to the debugpy_step_out rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block debugpy_step_out completely? +

Set action: deny in the PolicyLayer policy for debugpy_step_out. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides debugpy_step_out? +

debugpy_step_out is provided by the Debugpy MCP server (will-garrett/debugpy-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.