debugpy_autodiscover_target · Debugpy MCP: Risk & Policy

WHAT IT DOES

What debugpy_autodiscover_target does on Debugpy

AI agents invoke debugpy_autodiscover_target 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_autodiscover_target needs a policy

The description is empty, lowering confidence. However, based on the server's stated purpose of 'container autodiscovery' and 'process injection', and the sibling tools (attach, connect, evaluate, continue), this tool likely scans/probes running Docker containers to discover debuggable Python processes. Autodiscovery in this context likely involves active probing of containers and processes, placing it in Execute.

From the tool's definition Tool name 'debugpy_autodiscover_target' combined with server context of attaching debugpy to running Python processes inside Docker containers for process injection and autodiscovery

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

debugpy_autodiscover_target Rate-limited

debugpy_autodiscover_target 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_autodiscover_target": {
      "limits": [
        {
          "counter": "debugpy_autodiscover_target_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_autodiscover_target

What does the debugpy_autodiscover_target tool do? +

debugpy_autodiscover_target. 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_autodiscover_target? +

Register the Debugpy MCP server in PolicyLayer and add a rule for debugpy_autodiscover_target: 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_autodiscover_target? +

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

Can I rate-limit debugpy_autodiscover_target? +

Yes. Add a rate_limit block to the debugpy_autodiscover_target 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_autodiscover_target completely? +

Set action: deny in the PolicyLayer policy for debugpy_autodiscover_target. 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_autodiscover_target? +

debugpy_autodiscover_target 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.