start_eval_run
Start a new eval run. Define the test batch upfront with test cases (input, intent, expected behavior), then record results as each case is executed. Rule: no change ships without an eval improvement.
This record as markdown: /tools/io-github-homenshum-nodebench/start-eval-run.md
What start_eval_run does on Nodebench
AI agents invoke start_eval_run 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 start_eval_run is rated High
This tool initiates automated test execution across a batch of predefined test cases. While not destructive or financial, it executes external operations (test runs) whose effects depend on the test case definitions. The phrase 'record results as each case is executed' confirms active execution rather than passive reading.
From the tool's definition 'Start a new eval run' with 'test cases' and 'record results as each case is executed' indicates triggering and executing a defined test batch
Attacks that exploit this kind of access
The rule that runs start_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 start_eval_run, this is the rule to start with:
start_eval_run 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 start_eval_run call is checked against it from then on.
Questions about start_eval_run
Start a new eval run. Define the test batch upfront with test cases (input, intent, expected behavior), then record results as each case is executed. Rule: no change ships without an eval improvement. 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 start_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.
start_eval_run 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 start_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 start_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.
start_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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