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studio_finish

Finish screen recordings by producing MP4, GIF, and social crops with optional offline voiceover, music, and subtitles - all local, no API keys.

Instructions

Stop recording and produce the final files: closes the browser, strips residual dark frames (ffmpeg blackdetect → trim), transcodes to a share-ready MP4 (H.264, faststart), then — optionally — narrates the caption lines with a LOCAL voice (no cloud/keys), lays down a music bed, writes/burns subtitles, and exports vertical/square social crops, plus a GIF. Voice-over timing is auto-corrected for the frames the deblack step removed. Returns every produced file with size + duration. Without ffmpeg you still get the raw WebM.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gifNoAlso export a compact GIF (optional; default false).
nameYesBase file name for the video (without extension), e.g. 'onboarding-walkthrough'.
musicNoPath to a local audio file for a background music bed, looped and ducked under the voice-over (optional).
voiceNoSystem voice name (optional), e.g. macOS 'Samantha' or 'Daniel'; falls back to the OS default voice.
aspectsNoExtra social crops to export beside the 16:9 master, e.g. ['9:16','1:1'] for Reels/TikTok/feed. Padded with the theme background.
subtitlesNo'srt' writes a sidecar .srt from the caption lines; 'burn' also renders them into the picture (muted-autoplay social); 'none' (default).
voiceoverNoNarrate the caption / studio_narrate lines with a local, offline voice (macOS say · piper · espeak-ng). No account, no API key. Default false.
Behavior5/5

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

With no annotations provided, the description carries the full burden and excels. It discloses all important behaviors: closes the browser, strips dark frames, transcodes, local voice narration, music bed, subtitles, social crops, GIF generation, auto-correction of voice timing, and return of file info. This is very transparent.

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 appropriately sized for a complex tool with many features. It front-loads the core purpose and then expands. While lengthy, every sentence adds value. Could be slightly more concise, but highly efficient overall.

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?

Given the complexity (7 parameters, no output schema, no annotations), the description is remarkably complete. It covers the entire workflow, fallback case, and return value details. Nothing essential 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 baseline is 3. The tool description adds workflow context but does not add meaning beyond what the parameter descriptions already provide. No additional parameter details are given in the main description.

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 uses a specific verb (Stop recording and produce) and clearly identifies the resource (final files). It lists the major actions (closes browser, strips frames, transcodes, voice-over, subtitles, crops, GIF) and distinguishes from sibling tools like studio_start and studio_caption by being the finishing tool.

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?

The description implicitly indicates when to use (after recording, to finalize) and provides a fallback scenario ('Without ffmpeg you still get the raw WebM'). However, it does not explicitly exclude alternative tools or state when not to use this tool.

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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