Suggest fixes for common Python errors based on error messages.
AI agents call suggest_fix to retrieve information from MCP Pyrefly without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool reads error messages and returns fix suggestions. It is advisory in nature, producing output without side effects. No code is executed, no data is written or deleted. Severity is low because a misuse would at most surface incorrect suggestions.
From the tool's definition 'Suggest fixes for common Python errors based on error messages' — the tool provides suggestions/recommendations only, with no indication it writes, executes, or modifies any code or state.
Attacks that exploit this kind of access
Suggest fixes for common Python errors based on error messages. It is categorised as a Read tool in the MCP Pyrefly MCP Server, which means it retrieves data without modifying state.
Register the MCP Pyrefly MCP server in PolicyLayer and add a rule for suggest_fix: 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.
suggest_fix is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the suggest_fix 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 suggest_fix. 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.
suggest_fix 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.
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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