request_review_queue
Request local review for a task and place it in a review queue.
This record as markdown: /tools/todos/request-review-queue.md
What request_review_queue does on Todos
AI agents use request_review_queue to create or update resources in Todos, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Todos environment.
Why request_review_queue is rated Medium
This tool modifies the state of a task by placing it in a review queue, which is a reversible write operation (the task could be removed from the queue or its state changed back). It does not delete data, execute code, or involve financial transactions.
From the tool's definition 'Request local review for a task and place it in a review queue'
Attacks that exploit this kind of access
The rule that runs request_review_queue safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Todos, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For request_review_queue, this is the rule to start with:
request_review_queue stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Todos, apply this rule, and every request_review_queue call is checked against it from then on.
Questions about request_review_queue
Request local review for a task and place it in a review queue. It is categorised as a Write tool in the Todos MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Todos MCP server in PolicyLayer and add a rule for request_review_queue: 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 Todos. Nothing to install.
request_review_queue 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 request_review_queue 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 request_review_queue. 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.
request_review_queue is provided by the Todos MCP server (@hasna/todos). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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