check-prediction

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only says 'check the status' but does not clarify if the operation is read-only, whether it can be called repeatedly, or what the response format contains. Lack of side-effect information and return details leaves a 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 sentences, front-loaded with the purpose, and contains no fluff. Every phrase earns its place, making it highly concise and easy to skim.

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?

With no output schema, the description should explain what the tool returns (e.g., status values like processing, completed, failed). It only covers the trigger condition but lacks return behavior details, making it somewhat incomplete for a simple tool that is otherwise straightforward.

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 100%: the parameter 'prediction_id' is already described as returned from a previous run-model call. The description does not add extra semantics beyond the schema, so baseline of 3 applies.

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 verb 'Check' and the resource 'the status of a running prediction', which is specific and distinct from sibling tools like run-model, search-models, and list-models. It also includes the context of a prediction_id from a prior run-model call.

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 explicitly states when to use the tool: after a run-model call returns a prediction_id with status 'processing'. This provides clear usage context, though it does not mention exclusions or alternative tools, but for this polling function the trigger condition is well-defined.

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