qencode

get_job_status_detailed

Read-onlyIdempotent

Fetch the full, authoritative status of a transcoding job.

Like `get_job_status`, but follows the job's per-job master
`status_url` for the complete detail set: per-rendition output URLs,
sizes, bitrates, durations, and any `warnings`. Use this once a job is
finishing/finished (e.g. after `wait_for_job` returns `completed`) when
you need the concrete output artefacts rather than just the overall
`status`/`percent`.

Flow: the compact `/v1/status` is queried first to learn the
`status_url`; if present and safe, the master endpoint is queried for
the detailed view. If a job has no `status_url` yet (e.g. still
queued) or the URL fails the SSRF host check, the compact status is
returned unchanged — this tool never errors where `get_job_status`
would have succeeded.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`task_token`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`texts`	No
`audios`	No
`images`	No
`status`	No
`videos`	No
`percent`	No
`duration`	No
`warnings`	No
`status_url`	No
`api_version`	No
`source_size`	No
`error_description`	No

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already signal readOnly/idempotent/non-destructive, but the description adds substantial context: the two-step flow (compact status first, then master status_url), the SSRF host check, and the 'never errors where get_job_status would have succeeded' guarantee. This goes well beyond 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, then uses short paragraphs for comparison, usage, and flow. Every sentence adds valuable information without redundancy or fluff. Well-structured for quick scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Includes important edge cases (no status_url yet, SSRF failure), how it differs from get_job_status, and when to call it relative to wait_for_job. With an output schema present, return details are already covered, so the description covers everything else needed for correct tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the description does not mention task_token at all. The parameter name is self-explanatory (a token identifying the job), but the description provides no guidance on how to obtain it or any format expectations, so it fails to compensate for the low schema coverage.

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?

Description opens with 'Fetch the full, authoritative status of a transcoding job' — a specific verb and resource. It explicitly contrasts with get_job_status by focusing on per-rendition details via status_url, clearly differentiating the tool from its sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use advice: 'Use this once a job is finishing/finished (e.g. after wait_for_job returns completed) when you need the concrete output artefacts rather than just the overall status/percent.' Also names the alternative (get_job_status) and describes the fallback behavior, making the choice unambiguous.

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

A3.9/5.0

Disambiguation4/5

Most tools are clearly distinct (list_buckets vs list_objects, search vs fetch docs). Minor overlap exists between transcode_video and start_encode2_raw (both submit jobs) and between get_job_status and get_job_status_detailed, but the descriptions explicitly state when to use which, making misselection unlikely.

Naming Consistency4/5

Names overwhelmingly follow verb_noun (create_bucket, list_buckets, get_download_url, transcode_video). A few deviations like start_encode2_raw, wait_for_job, and download_url_to_bucket break the pure pattern, but the convention is still easily predictable.

Tool Count5/5

13 tools is well-scoped for a video encoding platform: bucket management, transcoding submission/status/wait, result retrieval, and docs search/read. Each tool serves a clear purpose without redundancy or bloat.

Completeness4/5

The set covers the main lifecycle: create bucket, ingest via copy, transcode (two entry points), poll status, fetch result, and generate download URLs. Missing cancel/delete operations for jobs and buckets are notable but not critical for core workflows, and the docs tools help fill knowledge gaps.

get_job_status_detailed

Input Schema

Output Schema

TDQS

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TDQS

Resources