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

Beyond the annotations, the description discloses that the beta platform covers user charge ($0.00), capacity limits still apply, and the response includes measured platform cost metadata. This adds useful behavioral context beyond the raw annotations, and nothing contradicts the false flags for readOnly, idempotent, or destructive hints.

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 core action is front-loaded in a single clear sentence, and the cost notes are tightly packed. The example URL is slightly long but earns its place by demonstrating the call syntax. Overall, every sentence provides value with minimal waste.

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 moderate complexity (4 params, output schema present), and the description covers the essential context: the operation, cost behavior, capacity limits, and an example call. Return values are covered by the output schema, and no critical invocation details are missing.

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% for all four parameters, so the description need not repeat their semantics. The example URL demonstrates the prompt and model parameters in practice, but it adds no meaning for system or max_tokens beyond what the schema already documents, matching the baseline 3.

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 action ('send a prompt'), a specific resource ('one specific large language model'), and a concrete outcome ('get the answer plus measured platform cost metadata'). The phrase 'one specific' inherently contrasts with sibling tools like compare_models and list_models, giving clear differentiation.

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 implies usage by emphasizing 'one specific' model, suggesting this is the single-model call rather than a comparison, but it never explicitly states when to use this over siblings or lists alternatives. The example URL and default-model note give some practical guidance but stop short of when-not-to-use conditions.

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