judge_request_retry
Request a retry, re-plan, escalation, or stop for a failed subtask.
This record as markdown: /tools/io-github-homenshum-nodebench/judge-request-retry.md
What judge_request_retry does on Nodebench
AI agents invoke judge_request_retry to trigger actions in Nodebench. 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.
Why judge_request_retry is rated High
This tool executes control flow operations (retry, re-plan, escalate, stop) on failed subtasks. It doesn't read/write data directly but triggers external operational decisions that affect agent workflow execution. The most severe applicable category is Execute, since it triggers external operations whose effects depend on arguments. Misuse could cause runaway retries or unwanted escalations, hence medium severity.
From the tool's definition 'Request a retry, re-plan, escalation, or stop for a failed subtask' — triggers orchestration-level control flow actions on subtasks
Attacks that exploit this kind of access
The rule that runs judge_request_retry safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Nodebench, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For judge_request_retry, this is the rule to start with:
judge_request_retry stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Nodebench, apply this rule, and every judge_request_retry call is checked against it from then on.
Questions about judge_request_retry
Request a retry, re-plan, escalation, or stop for a failed subtask. It is categorised as a Execute tool in the Nodebench MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Nodebench MCP server in PolicyLayer and add a rule for judge_request_retry: 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 Nodebench. Nothing to install.
judge_request_retry 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 judge_request_retry 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 judge_request_retry. 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.
judge_request_retry is provided by the Nodebench MCP server (nodebench-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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