llm_model_eval

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

With no annotations, the description covers caching, automatic scheduling, and output format, but does not explicitly state whether the tool is read-only or if it modifies any state.

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?

Concise, front-loaded with the main purpose, followed by key details (caching, scheduling, output). 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?

Given zero parameters, an output schema exists, and the description explains the output format and caching behavior. It is sufficiently complete for a simple evaluation tool.

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?

No parameters exist, so baseline score of 4 applies. The description confirms the tool takes no inputs, which is consistent.

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?

Clearly states it evaluates and benchmarks models with specific tasks (reasoning, code). However, it does not differentiate from the sibling tool 'llm_benchmark', which likely serves a similar purpose.

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 on manual vs automatic invocation and caching duration, but lacks explicit guidance on when to use this tool versus alternatives like 'llm_benchmark'.

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