Job status

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context by specifying that it parses ffmpeg output and returns a progress percentage, giving insight into how the status is determined beyond the annotation basics.

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?

Three concise sentences with no filler. The opening sentence states the purpose, the second explains the return behavior, and the third provides sequential usage guidance. Every sentence adds value.

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?

Given the simple parameter set, rich annotations, and presence of an output schema, the description fully covers what an agent needs: what statuses to expect, that progress is included, and how to proceed. No critical information is 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 the single job_id parameter, with a clear description. The tool description reinforces that the job_id comes from queueing work, adding workflow context that complements the schema without redundancy.

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 explicitly states the tool checks a queued job's progress and lists the possible statuses (queued, running, done, failed, cancelled). It clearly distinguishes from siblings like job_result (getting final result) and cancel_job.

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?

Provides explicit workflow guidance: poll this after queueing work, then call job_result once status is 'done'. This clearly establishes when to use it and what to do next, differentiating it from the related result retrieval tool.

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