Find cheapest models

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

No annotations are provided, so the description carries the burden. It discloses a key behavioral trait: the generic ranking excludes embeddings, OCR, and realtime models because they price different work, and explains how category adjusts the pool. This adds meaningful context beyond the obvious.

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?

Three sentences front-loaded with purpose, followed by a necessary exclusions caveat and a direct usage statement. No redundant content; every sentence 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?

The tool has 4 optional parameters and no output schema. The description covers scope, exclusion behavior, category-specific use, and intended use case, giving an agent enough to select and invoke correctly without additional context.

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 description coverage is 100%, so baseline is 3. The description adds value by illustrating category usage with 'embedding', clarifying the meaning of the ranking metrics, and noting the default behavior. This goes slightly beyond the schema's dry parameter descriptions.

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 uses a specific verb ('Find') and resource ('cheapest current models'), and details ranking options (input, output, blended). It also distinguishes itself from siblings by noting the generic ranking excludes embeddings/OCR/realtime and that category can target specific pools.

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 explicit guidance: 'Use to answer "what is the cheapest model for <use case>"' and explains when to pass a category. While it doesn't name alternatives, it gives clear context and exclusions that frame appropriate usage.

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