llm_model_eval

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

With no annotations, the description fully carries the burden. It discloses benchmark tasks, caching for 7 days, and return format. It could add more on resource usage or side effects, but is still transparent.

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 concise and well-structured: purpose first, then details on behavior, caching, usage, and return value. 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 no parameters and presence of an output schema, the description provides all necessary context: what models, what benchmarks, caching, manual/auto usage, and return format. It is complete for the tool's complexity.

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 tool has zero parameters, so the description adds meaning by explaining the tool's behavior and purpose beyond the empty schema. Baseline of 4 for zero params, but the description is excellent.

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 the tool evaluates and benchmarks all available local and remote models, specifying verb (evaluate, benchmark) and resource (all models). It distinguishes itself from siblings by its scope and caching behavior.

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 mentions it can be called manually or runs automatically weekly, providing usage context. However, it does not explicitly state when not to use it or compare with the sibling 'llm_benchmark'.

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