llm_benchmark

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

With no annotations, the description carries full burden. It discloses accuracy computation from llm_rate feedback and optional community export, but does not mention if the tool is read-only, has rate limits, or other behavioral traits. The existence of an output schema reduces the need to explain return values, but the description could be more transparent about potential delays or data freshness.

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?

The description is three concise sentences, front-loaded with the primary purpose, and efficiently explains data source and a conditional feature. Every sentence adds value without redundancy.

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?

Given zero parameters and an output schema, the description is largely complete. It covers purpose, data source, and a conditional feature. However, it could briefly mention that the output schema provides detailed breakdowns, but that is not required since the output schema exists.

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?

The input schema has zero parameters and 100% coverage, so baseline is 4. The description adds nothing about parameters (none exist), and that is appropriate since the tool requires no input.

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?

The description clearly states it shows routing accuracy benchmarks by task type, specifying the verb 'show' and the resource 'routing accuracy benchmarks'. It distinguishes itself from sibling tools like llm_model_eval and llm_quality_report by focusing specifically on routing accuracy with llm_rate feedback.

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?

The description implies usage by mentioning accuracy depends on llm_rate feedback and community export condition, but it does not explicitly state when to use this tool vs alternatives or provide when-not-to-use guidance. There is no reference to sibling tools.

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