benchmark_run

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so description carries full burden. It discloses the requirement for a recorded raw artifact and that model self-report doesn't set the bar. Missing details on side effects, idempotency, error handling, or whether it overwrites existing runs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two sentences with no waste. Front-loaded with main purpose, then additional constraints. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a tool with 3 required params and no output schema. Explains frozen benchmark concept, baseline role, and measurementRef requirement. However, lacks details on return value, success behavior, or error cases, leaving some gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 33% (only arm described). Description adds meaning by specifying measurementRef is a recorded raw artifact, and arm is baseline or hypothesis id, but runId remains unexplained. Some value beyond schema, but incomplete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool records a tool-measured run of an arm in a frozen benchmark, and specifies that the baseline arm sets the bar. It distinguishes from sibling tools like artifact_record and benchmark_propose by focusing on measurement runs, but does not explicitly differentiate from other benchmark-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides context that measurementRef must be a recorded raw artifact and model self-report never sets the bar, implying when not to use. However, lacks explicit when-to-use/when-not-to-use guidance or alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.