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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered. The description adds genuine behavioral value by disclosing the return payload shape (slugs, BenchGecko score, list price), which the annotations cannot convey.

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 tight sentences with zero filler. The purpose is front-loaded and the return-value information follows immediately, so every clause earns its place.

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?

With no output schema, the description correctly takes on the burden of describing the return contents (slugs, score, price). It is nearly complete for a search tool, but the unexplained 'limit' parameter and absent guidance on result volume leave a small gap.

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 only 50% - 'query' is documented in the schema, but 'limit' has no description anywhere. The description's 'by name, slug or lab' reinforces the query semantics but adds no syntax or default/range info for limit, so it does not fully compensate for the gap.

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 (Find) and resource (AI models) with scope (BenchGecko catalog) and the searchable fields (name, slug or lab). It also distinguishes itself from the generic 'search' sibling by being model-specific and names the downstream tools it feeds.

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?

Clearly signals the workflow context - results return slugs to use with get_model, cheapest_provider and compare_models - which tells the agent when this tool is the right entry point. It stops short of stating when not to use it or how it differs from the sibling 'search' tool.

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