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video_analyze

Extract transcript, metadata, scenes, audio, quality, chapters, and colors from any video. Works with local files or URLs, including streaming platforms.

Instructions

Comprehensive video analysis — transcript, metadata, scenes, audio, quality, chapters, colors.

Accepts a local file path or an HTTP/HTTPS URL. Direct video URLs (e.g. https://example.com/clip.mp4) are downloaded automatically. Streaming-platform URLs (YouTube, Vimeo, TikTok, Twitter/X, Instagram, Twitch, …) require yt-dlp (pip install yt-dlp). Each sub-analysis is independent so one failure will not abort the others.

Args: input_path: Local path or HTTP/HTTPS URL to the video. whisper_model: Whisper model size (tiny, base, small, medium, large, turbo). language: Language code for transcription (auto-detect if None). scene_threshold: Scene change sensitivity 0.0-1.0. include_transcript: Run speech-to-text via Whisper (requires openai-whisper). include_scenes: Detect scene changes and boundaries. include_audio: Analyse audio waveform, peaks, and silence regions. include_quality: Run visual quality check. include_chapters: Auto-generate chapter markers from scene changes. include_colors: Extract dominant colors and extended metadata. output_srt: Optional path to write SRT subtitle file. output_txt: Optional path to write plain-text transcript. output_md: Optional path to write Markdown transcript with timestamps. output_json: Optional path to write full JSON transcript data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_pathYes
whisper_modelNobase
languageNo
scene_thresholdNo
include_transcriptNo
include_scenesNo
include_audioNo
include_qualityNo
include_chaptersNo
include_colorsNo
output_srtNo
output_txtNo
output_mdNo
output_jsonNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses input types, dependencies, independent sub-analyses, and output options. While it could mention potential failures or rate limits, it covers essential behavioral traits beyond basic parameters.

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 front-loaded with a clear purpose and then structured with a concise paragraph on input types and dependencies, followed by a detailed parameter list. The parameter list is lengthy but necessary and well-organized.

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 tool's complexity (14 parameters, multiple sub-analyses, and dependencies), the description is complete. It covers input handling, prerequisites (yt-dlp, openai-whisper), independence of analyses, and output file options. An output schema exists, so return values are already documented.

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

Parameters5/5

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

Despite 0% schema description coverage, the description fully explains each parameter: `whisper_model` sizes, `language` auto-detect, `scene_threshold` range (0.0-1.0), and boolean flags for each analysis. This adds crucial meaning beyond the bare 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?

The description clearly states 'Comprehensive video analysis — transcript, metadata, scenes, audio, quality, chapters, colors.' It uses a specific verb (analyze) and resource (video) and distinguishes itself from sibling tools by being a one-stop analysis tool, unlike siblings that focus on individual aspects.

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?

Provides clear guidance on input types (local file or HTTP/HTTPS URL) and mentions dependencies (yt-dlp for streaming platforms). However, it does not explicitly indicate when to use this tool versus alternatives like `video_detect_scenes` or `video_ai_transcribe`, lacking exclusion criteria.

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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