estimate_cost

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention whether the operation is read-only, error conditions, return format, or any side effects. It only covers input semantics, leaving a significant transparency gap.

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 two concise sentences with a valuable example. It front-loads the main purpose, uses no unnecessary words, and is well-structured for quick comprehension.

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 a nested usage object, no output schema, and moderate complexity. The description covers input requirements and provides an example, but it does not explain the return value, such as cost format or any potential errors, leaving a notable completeness gap.

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?

The schema already provides descriptions for both parameters with 100% coverage, so the baseline is 3. The description adds a concrete example and clarifies LLM vs media usage, going beyond what the schema states and enhancing parameter understanding.

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 clearly states the tool estimates the cost of using an AI model, with a specific verb and resource. The example distinguishes it from sibling tools like compare_models or list_models by showing a single-model usage estimation.

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?

It provides explicit guidance on input structure for LLMs vs media models, which helps the agent know how to format the usage parameter. It does not name alternatives, but the context implies single-model estimation, and the example reinforces this.

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