dive_fix_verify
After fixing a bug, verify the fix by re-navigating to the affected route, comparing before/after state, and updating the bug status + changelog. This is the core flywheel step: Bug tagged → Code located → Code fixed → Fix verified → Changelog updated → Bug marked resolved. The agent should navig...
This record as markdown: /tools/io-github-homenshum-nodebench/dive-fix-verify.md
What dive_fix_verify does on Nodebench
AI agents invoke dive_fix_verify 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 dive_fix_verify is rated High
This tool executes browser automation via Playwright (navigate, screenshot), modifies bug status records, and writes changelog entries. It spans Write and Execute; Execute is the dominant category given the Playwright browser actions and the potential for arbitrary route navigation with side effects.
From the tool's definition re-navigating to the affected route, comparing before/after state, and updating the bug status + changelog... navigate to the route via Playwright, take a new screenshot
Attacks that exploit this kind of access
The rule that runs dive_fix_verify 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 dive_fix_verify, this is the rule to start with:
dive_fix_verify 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 dive_fix_verify call is checked against it from then on.
Questions about dive_fix_verify
After fixing a bug, verify the fix by re-navigating to the affected route, comparing before/after state, and updating the bug status + changelog. This is the core flywheel step: Bug tagged → Code located → Code fixed → Fix verified → Changelog updated → Bug marked resolved. The agent should navigate to the route via Playwright, take a new screenshot, and pass the results here. 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 dive_fix_verify: 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.
dive_fix_verify 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 dive_fix_verify 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 dive_fix_verify. 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.
dive_fix_verify 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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