Check code with enforcement - blocks progress until fixes are made.
AI agents invoke check_code_enforced to trigger actions in MCP Pyrefly. 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 does more than passively read or query; it enforces a behavioral constraint by blocking progress until fixes are made. This constitutes an external operation with side effects on the agent's execution flow and session state.
From the tool's definition 'Check code with enforcement - blocks progress until fixes are made' - triggers external enforcement behavior that controls agent flow
Attacks that exploit this kind of access
Check code with enforcement - blocks progress until fixes are made. It is categorised as a Execute tool in the MCP Pyrefly MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the MCP Pyrefly MCP server in PolicyLayer and add a rule for check_code_enforced: 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 Pyrefly. Nothing to install.
check_code_enforced 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 check_code_enforced 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 check_code_enforced. 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.
check_code_enforced is provided by the MCP Pyrefly MCP server (kimasplund/mcp-pyrefly). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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