create_review_comment
AI agents use create_review_comment to create or update resources in GitHub MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your GitHub MCP Server environment.
This tool creates new data (a review comment on a pull request) reversibly—comments can be edited or deleted. It modifies repository state but does not execute code, delete data irreversibly, or move money. Severity is medium because malicious review comments could mislead code review processes, but the impact is containable and reversible.
From the tool's definition Tool name 'create_review_comment' indicates creation of a comment on a pull request review. Sibling context (create_issue_comment, create_gist, create_pr_review) shows this server creates content in GitHub.
Attacks that exploit this kind of access
create_review_comment. It is categorised as a Write tool in the GitHub MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the GitHub MCP Server MCP server in PolicyLayer and add a rule for create_review_comment: 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 GitHub MCP Server. Nothing to install.
create_review_comment 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 create_review_comment 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 create_review_comment. 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.
create_review_comment is provided by the GitHub MCP Server MCP server (software-engineer-mj/github-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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