dashai_predict

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

The description adds significant behavioral context beyond the annotations: it explains the asynchronous nature, the need to poll with dashai_job_status, the FINISHED status requirement, and the exact JSON return format. It also notes that the request is rejected if the run is not finished. This fully discloses the tool's behavior.

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 concise and well-structured. It opens with a clear one-sentence summary, then provides async behavior, precondition, arguments, and return format in a logical order. No unnecessary filler exists.

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 one parameter and rich annotations, the description covers all essential aspects: what it does, when to use it, how to handle the asynchronous job, the required run status, and the exact response structure. It is complete enough for an agent to invoke correctly.

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 0% coverage on the top level, and the description compensates by explaining the 'params' object and the 'run_id' parameter, including that it must be a finished run. It reiterates the schema's description but adds context about the job_id and return payload. However, it doesn't add much beyond the schema's own field description for run_id.

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 action: 'Enqueues a prediction using the model of an already finished run.' This uses a specific verb ('enqueues') and resource ('prediction using a model'), and it distinguishes from sibling tools like dashai_train_model (training) and dashai_job_status (status checking).

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 clear context for when to use the tool: after a run has finished, and it explains the asynchronous workflow ('returns a job_id and the result is followed with dashai_job_status'). It also states a prerequisite (run must be FINISHED) and the rejection behavior. It does not explicitly mention alternatives, but the use case is well-defined.

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