AI agents invoke dap_step_over to trigger actions in Mcp 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.
Stepping over a line causes the debugged Python process to resume execution and run code whose side effects depend on what that code does. This is an Execute operation because it triggers execution of arbitrary code in the debugged program—the side effects are unpredictable without knowing the program's logic.
From the tool's definition dap_step_over advances execution of a running debugged program by stepping over the next line, which is an active control-flow operation that triggers external code execution via the debugpy Debug Adapter Protocol.
Documented attack patterns abuse exactly the kind of access dap_step_over gives an agent:
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_step_over:
{
"version": "1",
"default": "deny",
"tools": {
"dap_step_over": {
"limits": [
{
"counter": "dap_step_over_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}dap_step_over 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.
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Step over the next line on the active 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.
Register the Mcp Debugpy MCP server in PolicyLayer and add a rule for dap_step_over: 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.
dap_step_over is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the dap_step_over 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.
Set action: deny in the PolicyLayer policy for dap_step_over. 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.
dap_step_over 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.
Start from Mcp Debugpy, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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16 Mcp Debugpy tools catalogued and risk-classified — across an index of 43,000+ MCP servers.