debugpy_continue · Debugpy MCP: Risk & Policy

WHAT IT DOES

What debugpy_continue does on Debugpy

AI agents invoke debugpy_continue 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_continue needs a policy

The tool resumes execution of a paused/debugged Python process, which is an active operation that triggers external process execution. Misuse could cause a suspended process to continue running unintended code paths, making it an Execute-category action with medium severity.

From the tool's definition Resume execution

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

debugpy_continue Rate-limited

debugpy_continue 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_continue": {
      "limits": [
        {
          "counter": "debugpy_continue_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_continue

What does the debugpy_continue tool do? +

Resume execution. 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_continue? +

Register the Debugpy MCP server in PolicyLayer and add a rule for debugpy_continue: 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_continue? +

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

Can I rate-limit debugpy_continue? +

Yes. Add a rate_limit block to the debugpy_continue 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_continue completely? +

Set action: deny in the PolicyLayer policy for debugpy_continue. 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_continue? +

debugpy_continue 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.