get_job
Poll a video job until it is done. Free. Use after submit_video_job returns a job_id.
job_id: id returned by submit_video_job, e.g. a1b2c3d4e5f6
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
Autosophia Agent Video
TDQS
A4.1/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral burden. It discloses that it polls until done and is free, but omits important details such as the return value, failure behavior, or what 'done' means. This is a partial disclosure, not a full one.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise two-sentence description plus a parameter note. Front-loaded with purpose and usage. No unnecessary words or redundancies.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple single-parameter tool, but missing return-value information and behavior on failure. Without an output schema, the agent does not know what the response will contain. The description covers core usage but leaves clear gaps for a complete picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only a title 'Job Id' with no description (0% coverage). The description compensates by explaining job_id is the id returned by submit_video_job and gives an example format. This adds essential meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool polls a video job until it is done. Uses a specific verb ('poll') and resource ('video job'), and distinguishes it from siblings by referencing the workflow: 'Use after submit_video_job returns a job_id.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use: 'Use after submit_video_job returns a job_id.' This provides clear context and ties into the sibling tool flow. It does not explicitly mention alternatives or exclusions, but the usage context is strong enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A4.1/5.0
Disambiguation5/5
Each tool has a distinct role in the video generation workflow: submit creates, get_job polls, fetch_video retrieves the result, list_jobs lists history, and the remaining tools cover voices, payment, service info, and feedback. There is no meaningful overlap between tools.
Naming Consistency4/5
Most tools follow a verb_noun pattern (submit_video_job, get_job, fetch_video, list_jobs, list_voices), but payment_info and service_info use a noun_info pattern. This is a minor deviation; all names are clear, snake_case, and readable.
Tool Count5/5
With 8 tools, the set is well-scoped for a video generation service. It covers job submission, monitoring, retrieval, listing, voice selection, and two informational endpoints, without unnecessary bloat or a feeling of incompleteness.
Completeness4/5
The core lifecycle is covered: submit, poll, fetch, and list jobs, plus listing voices and payment guidance. The only notable gap is the lack of job cancellation or update operations, but agents can work around this since the service is fire-and-forget.