debugpy_breakpoint_plan · Debugpy MCP: Risk & Policy

WHAT IT DOES

What debugpy_breakpoint_plan does on Debugpy

AI agents invoke debugpy_breakpoint_plan 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_breakpoint_plan needs a policy

The description is empty, lowering confidence. Based on the server context (attaching debugpy to running Python processes, process injection) and the sibling tools (debugpy_attach, debugpy_continue, debugpy_evaluate), a breakpoint plan tool likely sets breakpoints in running processes, which is an Execute-category action — it triggers operations in external processes.

From the tool's definition Tool name 'debugpy_breakpoint_plan' on a server that 'provides tools for container autodiscovery, process injection, and generating breakpoint plans based on logs and metadata'

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

debugpy_breakpoint_plan Rate-limited

debugpy_breakpoint_plan 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_breakpoint_plan": {
      "limits": [
        {
          "counter": "debugpy_breakpoint_plan_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_breakpoint_plan

What does the debugpy_breakpoint_plan tool do? +

debugpy_breakpoint_plan. 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_breakpoint_plan? +

Register the Debugpy MCP server in PolicyLayer and add a rule for debugpy_breakpoint_plan: 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_breakpoint_plan? +

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

Can I rate-limit debugpy_breakpoint_plan? +

Yes. Add a rate_limit block to the debugpy_breakpoint_plan 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_breakpoint_plan completely? +

Set action: deny in the PolicyLayer policy for debugpy_breakpoint_plan. 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_breakpoint_plan? +

debugpy_breakpoint_plan 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.