AI agents invoke sessions_step_in to trigger actions in Debug-MCP. 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.
This tool controls execution flow of a running debug session by stepping into function calls. It triggers execution of code in an active debugging session, which falls under the Execute category. Misuse could cause unintended code execution, though it requires an active breakpoint to function.
From the tool's definition Step into the next function call (requires active breakpoint)
Attacks that exploit this kind of access
Step into the next function call (requires active breakpoint). It is categorised as a Execute tool in the Debug-MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Debug- MCP server in PolicyLayer and add a rule for sessions_step_in: 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 Debug-MCP. Nothing to install.
sessions_step_in 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 sessions_step_in 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 sessions_step_in. 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.
sessions_step_in is provided by the Debug- MCP server (kaina3/debug-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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