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lunara_start_subtitle_job

Run the full eight-layer subtitle pipeline on an audio or video URL you can already host. Produces a written-language track and a translated track. Prefer lunara_upload_video when the file is local.

Input Schema

NameRequiredDescriptionDefault
audioUrlYesA URL Lunara can download (public or pre-signed).
spokenLangNoWhat the speaker is speaking, e.g. yue-Hant-HK. Defaults to primaryLang.
primaryLangNoLanguage of the written track. Default yue-Hant.
secondaryLangNoLanguage of the translated track. Default en.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true and idempotentHint=false, so the safety and side-effect profile is covered structurally. The description adds the useful fact that this is a heavyweight eight-layer pipeline producing two tracks, but omits that it is asynchronous and that results must be retrieved via lunara_get_job / lunara_get_srt — a material gap for a tool whose name is 'start ... job'.

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?

Three short sentences, front-loaded with the action and its output, then the routing caveat. Nothing is padded and no sentence is redundant.

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?

With four params, no output schema, and non-idempotent job-start semantics, the description should say what comes back (a job handle) and how to get subtitles. It covers inputs and outputs conceptually but leaves the async lifecycle — the part an agent most needs — to inference from sibling names.

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%: audioUrl, spokenLang, primaryLang and secondaryLang each carry their own description and two have enums, so the schema does the heavy lifting. The description only reinforces audioUrl with 'a URL you can already host', adding marginal meaning beyond the schema's 'public or pre-signed'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb and resource ('Run the full eight-layer subtitle pipeline on an audio or video URL') plus the two artifacts it produces, which is far more than a restatement of the name. It differentiates from lunara_upload_video by input source, but leaves the near-identical sibling lunara_subtitle_from_url completely unaddressed, so an agent still cannot fully disambiguate.

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?

'Prefer lunara_upload_video when the file is local' is an explicit alternative with the selecting condition, which is exactly the right shape of guidance. However, the closest alternative (lunara_subtitle_from_url) is never mentioned, so the most likely wrong-tool choice remains unguided.

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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