dap_continue

THE RISK

High Risk

Why dap_continue needs a policy

This tool triggers resumption of a paused Python process execution. While debugging itself is legitimate, an AI agent invoking dap_continue without proper intent could allow arbitrary Python code to run to completion, potentially executing malicious logic.

From the tool's definition Tool continues execution of a debugged Python process. The description states 'Continue execution on the specified (or last stopped) thread,' and the server provides debugging capabilities via debugpy including running tests, setting breakpoints, and…

Documented attack patterns abuse exactly the kind of access dap_continue gives an agent:

POLICY

How to control dap_continue

PolicyLayer is an MCP gateway — it sits between your AI agents and Mcp Debugpy, and nothing reaches the server without passing your rules. This is the rule we recommend for dap_continue:

policy.json

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

dap_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.

Create a free account and register Mcp Debugpy — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

RATE-LIMIT THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

More Mcp Debugpy tools

Execute dap_launch dap_launch Execute dap_shutdown Terminate the current DAP adapter session. Execute dap_step_in Step into the next function call on the active thread. Execute dap_step_out Step out of the current function on the active thread. Execute dap_step_over Step over the next line on the active thread. Execute dap_wait_for_event Wait for a specific DAP event (e.g., 'stopped'). Execute run_tests_focus Run a focused subset: pytest -k <keyword> with JSON report. Execute run_tests_json run_tests_json

All 16 Mcp Debugpy tools →

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Go deeper

High-risk tools in Mcp Debugpy → Every high-risk tool this server exposes, in one view.
All high-risk MCP tools → Every high-risk tool across the catalogue, and the policy pattern that contains them.
The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Agent sandbox → Bounding what an agent can execute and where the boundary should sit.
Least privilege for MCP → Granting agents the minimum tool surface the task needs.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about dap_continue

What does the dap_continue tool do? +

Continue execution on the specified (or last stopped) thread. It is categorised as a Execute tool in the Mcp 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 dap_continue? +

Register the Mcp Debugpy MCP server in PolicyLayer and add a rule for dap_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 Mcp Debugpy. Nothing to install.

What risk level is dap_continue? +

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

Can I rate-limit dap_continue? +

Yes. Add a rate_limit block to the dap_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 dap_continue completely? +

Set action: deny in the PolicyLayer policy for dap_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 dap_continue? +

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

Enforce policy on every Mcp Debugpy tool call.

Start from Mcp Debugpy, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →