llm_model_eval

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

With no annotations, the description discloses key behaviors: caching for 7 days, impact on routing priorities, auto-scheduling, and return format (quality scores, latency metrics). It does not explicitly state if it is read-only or requires permissions, but covers most important aspects.

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 with three focused paragraphs: purpose, scheduling, and returns. Every sentence adds value, with no redundancy. The first sentence immediately captures the tool's core action.

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 presence of an output schema, the description adequately covers inputs, behavior (caching, auto-run), and outputs. It could have mentioned prerequisites or permissions, but overall is complete for this 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 input schema has zero parameters, so no additional semantics are needed. The description correctly explains that the tool operates on all models without user input, making the schema self-sufficient. Baseline of 4 applies.

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 models, specifying verb (evaluate/benchmark) and resource (all local and remote models). It distinguishes from siblings by emphasizing comprehensive coverage across Ollama, Codex, and APIs, and mentions integration with routing optimization.

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 provides explicit context: can be called manually or runs automatically weekly. However, it lacks when-not-to-use guidance or comparison to similar siblings like llm_benchmark or llm_analyze, which would help avoid misuse.

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