search_ai_models

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

No annotations are provided, so the description carries the full burden. It discloses what data domains are searched (input/output/caching pricing, context windows), which is useful, but says nothing about read-only nature, result format, pagination, or no-match behavior.

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?

A single dense sentence with the key content (pricing dimensions, providers, context windows) front-loaded and no filler. It earns its place, though the provider enumeration is long and partly redundant with the schema.

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?

Both parameters are optional and fully documented by the schema, and there is no output schema. The description covers the domain well enough to invoke the tool, but for a search tool with no annotations or output schema it could say more about what results look like.

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 100%, so the schema already documents both optional parameters fully; baseline is 3. The description adds a provider list, but it omits 'google' which the schema explicitly supports, making the description slightly less complete than the schema rather than additive.

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 (search) and resource (token pricing and context windows for AI LLMs), and enumerates the pricing dimensions covered. It is distinguishable from compare_cloud_pricing by scope (AI model token pricing vs cloud infrastructure), though the sibling 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?

Usage is implied by the scope statement – an agent can infer this is for looking up LLM pricing/context data. However, there is no explicit when-to-use guidance, no mention of compare_cloud_pricing as the alternative for cloud pricing, and no exclusions.

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