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Spoken words

lab_wortzeiten
Read-only

Every spoken word with its start and end time, for a clip (video_id), an audio track (audio_id) or an uploaded file (project_id + asset_key). Use it to cut pauses without cutting into a word, to time captions, or to land a graphic on an exact word — instead of guessing seconds from the waveform. Up to 20 min of sound per source; the first call transcribes (Fish Audio, about 0.36 USD per audio hour, booked to the operator, not your credits), every later call for the same sound is served from the cache for free. Returns at most 300 s of words per page; continue with weiter_von_s. Speech recognition spells names the way they sound — check names before you put them on screen.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bis_sNoEnd of the page; at most 300 s after von_s
von_sNoStart of the page in seconds (default 0)
spracheNoSpoken language (default de) — the same list the backend accepts
audio_idNoAudio track id (from lab_list_audio)
video_idNoGenerated video id — its own soundtrack. Give exactly one of video_id, audio_id, asset_key
asset_keyNoStorage key of an uploaded sound or video file (from lab_add_media). Needs project_id
project_idNoProject the asset_key belongs to — required with asset_key

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

readOnlyHint=true covers the safety profile, yet the description adds the traits that actually matter operationally: 20 min source cap, first-call transcription via Fish Audio with cost (0.36 USD/audio-hour billed to operator, not credits), free cache hits thereafter, 300 s page limit with `weiter_von_s` continuation, and the name-spelling caveat. This is exactly the extra context annotations cannot carry.

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?

Front-loads what is returned (word + start/end time) then sources, then why, then cost/paging caveats. Dense but every clause carries information; the final name-spelling warning is a slight afterthought but still useful.

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?

No output schema and no annotations beyond readOnlyHint, so the description carries the load — and it does: return shape (words with start/end), cost/caching model, latency implication of first transcription, paging limit, and the source-selection rule. Nothing an agent needs to call and interpret it is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema documents von_s/bis_s/sprache/ids on its own; baseline 3 applies. The description reinforces the mutually-exclusive source-id semantics and the paging pair (300 s / weiter_von_s) but adds no syntax 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?

States a specific verb+resource ('every spoken word with its start and end time') and enumerates the three source modes (video_id, audio_id, asset_key) that distinguish it from siblings like lab_wellenform or lab_list_audio. An agent can immediately tell it returns word-level timings, not a waveform or an asset list.

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?

Explicit use cases are named ('cut pauses without cutting into a word, time captions, land a graphic on an exact word — instead of guessing seconds from the waveform'), which also implicitly rejects the waveform-guessing alternative. The multi-source applicability is spelled out per id type.

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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