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

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds the return value structure ('recommended model, live cost estimate, savings vs current provider, signup link') and highlights the 'live' nature of the cost estimate, which provides behavioral context beyond the annotations.

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 three sentences: purpose, usage, and return value. It is front-loaded with the most critical information and contains no redundant or filler content.

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?

For a recommendation tool with three parameters and an output schema, the description covers the essential context: what it does, when to use it, and what it returns. The output schema handles detailed return format, and annotations cover safety, making this description complete.

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 the parameters are already fully documented. The description does not add additional semantic detail about parameters beyond what the schema provides; it only indirectly references current_provider via 'savings vs current provider.' Baseline 3 is appropriate.

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 opens with a specific verb ('Suggest') and resource ('best and cheapest AI model for a given task'), clearly stating the tool's core function. This distinguishes it from sibling tools like get_available_models or get_pricing by focusing on a recommendation that combines quality and cost.

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 second sentence provides a clear usage scenario: 'Use this when helping users choose AI providers or optimize inference costs.' While it does not explicitly name alternative tools or when-not-to-use conditions, the context is specific enough to guide selection among the listed siblings.

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