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Glama

Fatiar vídeo em frames (scrub)

extrair_frames

Extract and prepare scroll-driven frame sequences from videos: trims static segments, exports JPEG/WebP frames, and generates fallback MP4s for immersive websites.

Instructions

Pipeline completo do prompt validado: corta automaticamente os frames parados, avisa sobre cortes secos, fatia ~200 frames (1600–1920 px, JPEG q3) em frames//frame_0001.jpg, gera o MP4 leve de fallback (mobile) e o pôster (movimento reduzido) e registra a sequência em imersao.manifest.json. Vários vídeos = clipes encadeados unidos numa só jornada. O site passa a usar o scrub real automaticamente.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fimNoForça o fim em segundos
nomeNoNome da sequência (deve bater com data-im-seq do capítulo). Padrão: nome do arquivo.
videoNoUm MP4 (absoluto ou relativo ao projeto, ex.: media/brutos/giro.mp4)
apararNoauto = corta trechos parados (padrão)
inicioNoForça o início em segundos
videosNoVários MP4 em ordem para uma jornada encadeada
formatoNo
larguraNoLargura em px (padrão 1920)
projetoYesPasta do projeto do site
quadrosNoQuantidade de frames (padrão 200)
mp4_scrubNoGera também um MP4 all-intra para scrub por vídeo
qualidadeNoJPEG q (2–5; padrão 3)
interpolarNoInterpola para 48 fps localmente (lento; para movimento rápido)
mp4_fallbackNoGera media/<nome>.mp4 leve para celular (padrão true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses automatic trimming of still segments, hard-cut warnings, concrete output paths and formats, and the side effect that the site will start using the real scrub immediately. Gaps remain on overwrite behavior (does re-running replace frames/<nome>/?) and permissions/runtime (interpolation is called 'lento'), but the side-effect surface is unusually well surfaced.

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?

Three sentences, front-loaded with the pipeline purpose and then the outputs; every clause carries factual content rather than filler. It is dense and jargon-laden ('prompt validado', 'imersao.manifest.json'), and the middle sentence strings five distinct side effects together, which slightly hurts scannability.

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?

For a 14-parameter, no-output-schema, no-annotation tool, the description covers the essential unknowns: what gets written where, which auxiliary artifacts appear, and that the site picks up the scrub automatically. It stops short of covering failure modes, permissions and whether existing frame folders are replaced, but an agent has enough to invoke it correctly.

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 93%, so the schema already documents nearly every one of the 14 parameters. The description adds concrete defaults that map to quadros, largura and qualidade ('~200 frames (1600–1920 px, JPEG q3)') and confirms the videos chaining semantics, but adds nothing for aparar, inicio, fim, interpolar or mp4_scrub beyond the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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+resource (cut a video into ~200 frames) and enumerates the full set of artifacts produced (frames/, fallback MP4, poster, imersao.manifest.json), so the agent knows exactly what the tool does. It does not, however, distinguish itself from close siblings such as extrair_quadro, analisar_video or folha_de_contato, which likely overlap in the video-to-image space.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The opening phrase 'Pipeline completo do prompt validado' implies where the tool sits in the workflow (the step after a validated visual prompt), and the note about multiple videos forming chained clips hints at the batched mode. There is no explicit when-to-use, when-not-to-use, or named alternative among the many sibling tools, so selection is left largely to inference.

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