prediction_pricing

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates a read-only getter operation ('Get pricing information'), implying no side effects, but it does not disclose details such as whether the data is real-time, cached, or the exact set of 'AI prediction services' covered. The description is safe but lacks depth for a zero-annotation context.

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 a single, front-loaded sentence that immediately states the tool's purpose. It has no redundant words and is appropriately sized for a tool with no parameters. Every word earns its place, making it highly concise.

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?

While the tool is simple (zero parameters, no output schema), the description is minimal. It tells the agent what is returned (pricing info) and the scope ('all AI prediction services'), but it does not explain what constitutes 'AI prediction services' or what the return format might be. Given the absence of an output schema, a bit more detail on the response content would improve completeness, though the current level is minimally adequate.

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 input schema has zero parameters, so per the rubric the baseline is 4. The description does not need to explain any parameters because there are none, and it adds no parameter-related information beyond what the empty schema implies. The baseline score is appropriate since there is nothing to compensate for.

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's function: 'Get pricing information for all AI prediction services.' It uses a specific verb ('Get'), a clear resource ('pricing information'), and a defined scope ('all AI prediction services'). This distinguishes it from sibling tools like 'prediction_stats' and 'prediction_assets' which deal with other aspects, and no other tool in the sibling list explicitly mentions pricing.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or alternative tools for similar use cases. The only sentence merely describes the action, leaving the agent to infer when this should be invoked.

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