debugpy_connect · Debugpy MCP: Risk & Policy

WHAT IT DOES

What debugpy_connect does on Debugpy

AI agents invoke debugpy_connect 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_connect needs a policy

The server description indicates this server attaches debugpy to running Python processes in Docker containers. A 'connect' tool in this context almost certainly establishes a debug connection to a live process, which is an Execute-level operation with significant blast radius (attaching a debugger to production containers). Confidence is lowered because the tool description is empty.

From the tool's definition Tool name 'debugpy_connect' on a server that 'enables agents to attach debugpy to running Python processes inside Docker containers' — connecting a debugger to a live process is an active external operation.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

debugpy_connect Rate-limited

debugpy_connect 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_connect": {
      "limits": [
        {
          "counter": "debugpy_connect_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_connect

What does the debugpy_connect tool do? +

debugpy_connect. 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_connect? +

Register the Debugpy MCP server in PolicyLayer and add a rule for debugpy_connect: 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_connect? +

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

Can I rate-limit debugpy_connect? +

Yes. Add a rate_limit block to the debugpy_connect 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_connect completely? +

Set action: deny in the PolicyLayer policy for debugpy_connect. 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_connect? +

debugpy_connect 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.