AI agents use suggest_reviewers to create or update resources in Pr — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Pr environment.
An AI agent can call suggest_reviewers faster than any human can review — one bad instruction and it creates or modifies resources in Pr by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
Suggests code reviewers based on Git contribution history of modified files in the current branch. Automatically analyzes the working directory. It is categorised as a Write tool in the Pr MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Pr MCP server in PolicyLayer and add a rule for suggest_reviewers: 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 Pr. Nothing to install.
suggest_reviewers is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the suggest_reviewers 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_reviewers. 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_reviewers is provided by the Pr MCP server (valentin-harrang/pr-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.