complete_eval_run
Finalize an eval run and compute aggregate scores. Returns pass rate, average score, failure patterns, and improvement suggestions.
This record as markdown: /tools/io-github-homenshum-nodebench/complete-eval-run.md
What complete_eval_run does on Nodebench
AI agents use complete_eval_run to create or update resources in Nodebench, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Nodebench environment.
Why complete_eval_run is rated Medium
This tool performs a state-changing operation by finalizing an evaluation run, which is a reversible write action. It computes and likely persists aggregate metrics. While it produces outputs, the primary effect is transitioning an eval run from an active/incomplete state to a completed state with computed results.
From the tool's definition Tool name 'complete_eval_run' and description 'Finalize an eval run and compute aggregate scores' indicates it modifies the state of an evaluation run (marking it complete) and generates/stores computed results (pass rate, average score, failure patterns,…
Attacks that exploit this kind of access
The rule that runs complete_eval_run 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 complete_eval_run, this is the rule to start with:
complete_eval_run 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 Nodebench, apply this rule, and every complete_eval_run call is checked against it from then on.
Questions about complete_eval_run
Finalize an eval run and compute aggregate scores. Returns pass rate, average score, failure patterns, and improvement suggestions. It is categorised as a Write tool in the Nodebench MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Nodebench MCP server in PolicyLayer and add a rule for complete_eval_run: 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.
complete_eval_run 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 complete_eval_run 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 complete_eval_run. 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.
complete_eval_run 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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