compare_models_cost

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses defaults for 'models' and the meaning of 'expected_output_tokens,' but does not mention whether this is a read-only operation, if it makes external API calls, or any limitations (e.g., pricing database dependence).

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 concise and well-structured, starting with a one-sentence purpose followed by a simple Args list. No wasted words; every sentence adds value.

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 three parameters, an output schema exists, and the description covers all parameter semantics and the core purpose. It leaves little unexplained, though it could benefit from noting when to prefer this over single-model tools like count_tokens or estimate_cost.

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 0%, so the description must compensate. It provides clear semantic meaning for all three parameters: text is the input, models is a list with a default behavior, and expected_output_tokens is a rough response-length guess applied uniformly.

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 states 'Compare token count and estimated cost for the same text across several models,' which clearly identifies the tool's purpose with a specific verb and resource. This differentiates it from sibling tools like count_tokens (single model) and estimate_cost (single model).

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 description clearly implies when to use the tool: when comparing token counts and costs across multiple models. It does not explicitly mention alternatives or exclusions, but the purpose itself strongly signals the use case relative to the sibling tools.

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