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export_preset

Encode a source video into a platform-ready file for YouTube, Instagram Reels, TikTok, X, or other presets by reframing, capping fps, and encoding H.264 at the target bitrate; returns the output path.

Instructions

Encode a platform-ready file for youtube, instagram_reel, tiktok, x, square, steam_trailer, or vertical_reel. Reframes to the platform aspect, caps fps, and encodes H.264 at the platform bitrate with faststart. Returns the output path.

Input Schema

NameRequiredDescriptionDefault
fitNoHow to fit the source into the platform aspect: pad (default) or crop.
inputYesPath to the source video.
outputNoOptional output path. Defaults under SCREENCAST_HOME/edits.
platformYesTarget platform. Sets aspect, resolution, fps, and bitrate. steam_trailer: 1920x1080, H.264 High, padded 5.5 Mbps CBR, AAC 48 kHz at -14 LUFS. vertical_reel: 1080x1920 with the footage centred over a blurred copy of itself, audio at -14 LUFS (fit is ignored).
overwriteNoAllow replacing an existing file at the output path (default false).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.32.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does substantial work: it discloses the transform pipeline and that the tool returns the output path. It omits the failure mode when an existing output file is present (the overwrite default) and any note about encode duration, which a writing tool with zero annotation coverage would ideally state.

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?

Two sentences, no filler. The deliverable and its platform targets come first, with the encoding details and return value following in a compact clause.

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

Completeness4/5

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

With full schema coverage and a stated return value, an agent has what it needs to invoke this correctly. The remaining gap is behavioral rather than structural: no guidance on overwrite collisions or runtime expectations for a mutation that writes a new file.

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 all five parameters are already documented, including the platform presets and the fit/overwrite defaults. The description adds no parameter meaning beyond restating the platform names the schema enum already lists, so baseline 3 applies.

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 and resource ('Encode a platform-ready file') and enumerates the exact platform targets, plus the transformations applied (reframe, fps cap, H.264, faststart). This clearly separates it from siblings like reframe, convert, and compress, which modify video without producing a platform-targeted deliverable.

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 enumerated platform list makes the intended use case obvious: this is the tool to pick when a deliverable must match a named platform's spec. It gives no explicit exclusions, though — nothing says to use convert or scale instead for non-platform aspect ratios, or how this differs from reframe plus convert chained.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.