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

Annotations are present (readOnlyHint=false, idempotentHint=false, destructiveHint=false), and the description adds meaningful context beyond them: the platform currently covers the user charge, capacity limits still apply, and the response includes measured cost metadata. This gives an agent a clearer sense of the operational and cost behavior without contradicting any annotation.

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, each earning its place: the core purpose, a key cost/limit caveat, and a concrete example. The main action is front-loaded, and there is no filler or repetition of schema 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 tool with four simple parameters, 100% schema coverage, and an output schema, the description covers the key behavioral points: what it does, current pricing, capacity caveat, and how to call it. It does not explain how to choose among sibling model-related tools, but that is a use-case guidance gap rather than a critical completeness gap for invoking this tool.

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 prompt, model, system, and max_tokens. The description's example URL adds a concrete usage pattern for prompt and model, but it does not add substantial semantic detail beyond what the schema provides. The baseline of 3 applies because the schema carries the descriptive burden.

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 a specific verb ('Send'), a precise resource ('one specific large language model'), and the expected outputs ('the answer plus measured platform cost metadata'). The phrase 'one specific' distinguishes it from sibling tools like compare_models, and the cost-metadata mention sets it apart from model_costs, so an agent can tell this tool apart without reading schemas.

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 gives useful operational context ('beta platform covers the user charge ($0.00); capacity limits still apply') and a concrete example, but it never explicitly states when to use this tool versus alternatives such as compare_models or list_models. Usage guidance is implied by the phrase 'one specific large language model' rather than spelled out.

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