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

Annotations already declare read-only, idempotent, non-destructive, so the safety profile is covered. The description adds genuine value by disclosing result ordering ("most widely offered first") and that output is slugs, but it says nothing about match semantics (fuzzy vs. exact) or behavior when nothing matches.

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?

Two sentences, front-loaded with the search intent, with ordering and the limit range appended compactly. Nothing is redundant.

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?

A small two-parameter tool with an output schema, or annotations covering safety; the description supplies the range, examples, and downstream usage. Only search matching semantics are left unstated.

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 0%, so the description carries the burden. It supplies useful query examples and bounds limit to 1-10, which the schema does not state. Still, neither parameter's matching or truncation behavior is explained, so compensation is partial.

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?

States a specific verb and resource ("Search the LLM catalog by name") and clarifies the return artifact (model slugs). It does not collide with any sibling, since none of the others perform lookup, but the differentiation is implicit rather than stated.

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?

Names the downstream consumers (cheapest_model_route, gpus_needed_for_model), which tells an agent this is a resolution step that must precede those calls. No explicit when-not or handling of no-match results, so it stops short of full routing guidance.

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