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Python
Local

video_analyze

Analyze local files or video URLs to extract transcripts, metadata, scenes, audio, quality, chapters, and colors for content review, indexing, or editing.

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

NameRequiredDescriptionDefault
languageNo
output_mdNo
overwriteNoExplicitly allow replacing existing media. Existing non-media files are protected.
input_pathYes
output_srtNo
output_txtNo
output_jsonNo
include_audioNo
whisper_modelNobase
include_colorsNo
include_scenesNo
include_qualityNo
scene_thresholdNo
include_chaptersNo
include_transcriptNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.16.4
    • addedInput schema / properties / overwrite
      Added value: +{
+  "default": false,
+  "description": "Explicitly allow replacing existing media. Existing non-media files are protected.",
+  "title": "Overwrite",
+  "type": "boolean"
+}
  2. Changed1 schema field changedv1.16.0
    • addedInput schema / additionalProperties
      Added value: +false
  3. Addedv1.5.1

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose two non-obvious traits: URLs are downloaded automatically, and each sub-analysis is isolated so a single failure won't abort the run. It omits other important behavior — that the tool writes files to the four output_* paths, that Whisper transcription is heavy/slow and may require model downloads, and what happens when include_transcript is true but openai-whisper isn't installed (only the dependency is noted in the Args line, not the failure mode).

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 capability summary is front-loaded, then a short input/dependency paragraph, then a compact Args block — a sensible shape for 15 parameters. The Args list is long but each line is one clause and earns its place; nothing is padded.

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?

An output schema exists, so return values needn't be described, and the description covers inputs, dependency requirements, and failure isolation adequately for a tool this size. What's missing is cost/runtime guidance (all six sub-analyses default to true, including Whisper) and acknowledgment that the output_* parameters cause file writes subject to overwrite protection.

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

Parameters4/5

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

Schema coverage is only 7%, so the description must compensate, and it largely does: 14 of 15 parameters get a one-line meaning, including the whisper_model value set (tiny…turbo) and scene_threshold's 0.0–1.0 range, neither of which the schema constrains. The only gap is 'overwrite', which is covered by its own schema description rather than this text.

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?

The opening line names a specific verb and resource (video analysis) and enumerates exactly what is produced: transcript, metadata, scenes, audio, quality, chapters, colors. That is unambiguous. It stops short of distinguishing itself from the many overlapping siblings (video_ai_transcribe, video_detect_scenes, video_auto_chapters, video_quality_check, image_extract_colors), so an agent can't tell from this line alone why it should pick the combined tool over a single-purpose one.

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?

Usage context is present but indirect: it explains input forms (local path or HTTP/HTTPS URL), that direct video URLs are downloaded, and that streaming platforms need yt-dlp. There is no explicit when-to-use/when-not statement and no routing to alternatives, which matters here because the sibling list contains dedicated tools for nearly every sub-analysis this one bundles.

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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