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qencode

by com.qencode

transcode_video

Submit a transcoding job.

Args:
    source: URL of the input video (https://, s3://, or `tus:<uuid>`).
    outputs: list of format-spec dicts. Each MUST have an `output` field
        whose value is one of: mp4, webm, advanced_hls, advanced_dash,
        webm_dash, repack, mp3, m4a, hls_audio, flac, gif, thumbnail,
        thumbnails, smart_thumbnail, metadata, speech_to_text, vmaf,
        video_intelligence, ai_detection.
        The OUTER array is named `format` in the Qencode schema (this
        tool wraps it for you). The INNER STRING field naming the type
        is `output` — NOT `format`. This is the most common composition
        mistake. Example of a valid entry:
            {
                "output": "mp4",
                "video_codec": "libx264",
                "audio_codec": "libfdk_aac",
                "resolution": 720,
                "optimize_bitrate": 1,
                "audio_bitrate": 128,
                "destination": {"url": "s3://..."}
            }
        For HLS/DASH ABR, put per-rendition params on each entry of an
        inner `stream[]` array (not on the format object directly).
        Output-specific required fields (see matching recipe):
            advanced_hls / advanced_dash / webm_dash / hls_audio —
                non-empty `stream[]` of objects. A bare
                `{"output": "advanced_hls"}` is rejected. Fetch
                `qencode://recipe/hls_abr` (or `audio_outputs` for
                `hls_audio`) before composing.
            vmaf — `distorted` URL of the encoded video; `source` is the
                reference original (encoder v1 is auto-selected).
            video_intelligence — `mode` one of description, categorization,
                moderation, search, custom (NOT `features`). Source must be
                https:// and meet duration minimums (description etc. ≥10s,
                search ≥4s) — check via metadata or tell user if too short.
        Example vmaf entry:
            {
                "output": "vmaf",
                "distorted": "https://example.com/encoded.mp4",
                "destination": {"url": "s3://.../vmaf.json"}
            }
        Example HLS entry (params on `stream[]`, not on the format object):
            {
                "output": "advanced_hls",
                "segment_duration": 6,
                "stream": [{
                    "video_codec": "libx264",
                    "audio_codec": "libfdk_aac",
                    "resolution": 720,
                    "framerate": "30",
                    "keyframe": "60",
                    "optimize_bitrate": 1,
                    "audio_bitrate": 128
                }]
            }
        Example video_intelligence entry:
            {
                "output": "video_intelligence",
                "mode": "description",
                "destination": {"url": "s3://.../vi/"}
            }
    payload: optional opaque string echoed back in callbacks.

`encoder_version` is injected automatically when omitted: `2` by default,
`1` when any output is `vmaf`. Stitch jobs (multi-source `stitch` array)
are not supported here — use `start_encode2_raw` with `encoder_version: 2`
per `qencode://recipe/stitching`.

Other composition defaults in this server's instructions (libfdk_aac,
optimize_bitrate, per-stream ABR params, etc.) still belong in each
`outputs[]` entry — consult the matching recipe via
`search_qencode_docs` + `fetch_qencode_doc` before submitting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
outputsYes
payloadNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
status_urlNo
task_tokenYes
upload_urlNo

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only say readOnlyHint=false, idempotentHint=false, destructiveHint=false. The description goes further by explaining that encoder_version is injected automatically, that vmaf forces encoder v1, that payload is echoed back in callbacks, and that certain output types require specific structures. It also warns about the common `format` vs `output` mistake, which is a valuable behavioral caveat beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is lengthy but well-structured: it front-loads the core action, then breaks down parameters with examples and callouts. Nearly every sentence carries operational weight. It is slightly verbose in repeated references to recipes, and some examples could arguably be shortened, but the structure makes the density manageable.

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?

For a tool with complex nested outputs and no schema-level enum or description coverage, this definition is remarkably complete. It covers all parameters, valid output types, required sub-fields, unsupported combinations, default encoder selection, and points to recipes for remaining details. The output schema is marked present, so return-value documentation is not a gap.

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

Parameters5/5

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

Schema description coverage is 0%, so the description carries full parameter documentation burden. It defines source URL forms, enumerates every valid output value, explains the required fields per output type, gives multiple valid examples, and describes the optional payload parameter. This fully compensates for the schema's lack of detail.

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?

The description opens with a specific action and resource: 'Submit a transcoding job.' It also distinguishes itself from the sibling start_encode2_raw by explicitly stating that stitch jobs are not supported here and should go to that tool. The scope is clear and not confused with sibling tools.

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?

It provides explicit when-to-use guidance: use recipes via search_qencode_docs + fetch_qencode_doc before composing outputs, and use start_encode2_raw for stitch jobs with encoder_version 2. It also gives conditional requirements for vmaf, video_intelligence, and HLS/DASH outputs, making the decision boundaries concrete.

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.

Resources