fetch_video
Fetch the finished video path and size once the job is done. Free.
job_id: id returned by submit_video_job
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
Fetch the finished video path and size once the job is done. Free.
job_id: id returned by submit_video_job
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral details itself. However, it only states 'Free' and the fetch action. It does not explain what happens if the job is not finished, whether it polls, or what error responses may occur. This leaves the agent without essential behavioral expectations.
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 minimal and front-loaded with the main purpose. The second part directly explains the single parameter. Every sentence provides useful information, and there is no redundant content.
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 simple fetch tool with one parameter, the description covers the key aspects: what is fetched, when (once job done), and where to get the job_id. It does not describe the output format, but no output schema exists, so this is a minor gap. Overall, it is sufficiently complete for its complexity.
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 schema only lists job_id as a required string with title 'Job Id'. The description adds meaning by stating 'id returned by submit_video_job', giving the agent a direct source for the parameter. This compensates for the complete lack of schema descriptions (0% coverage).
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 states a clear action: 'Fetch the finished video path and size once the job is done.' This specifies the exact resource (finished video path and size) and the condition (once job is done), distinguishing it from sibling tools like get_job or list_jobs.
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 phrase 'once the job is done' provides clear timing for use, indicating that this tool should be used after job completion. It does not explicitly name alternatives or exclusions, but the context implies it is for retrieving results, not checking status (which likely belongs to get_job).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.