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Extract visual context from videos by querying transcripts, auto-detecting demonstrations, or specifying timestamps. Assist mode identifies gaps where user screenshots are needed.

Instructions

ONLY call this tool when the user explicitly asks for visual context, screenshots, or frames. Do NOT call it alongside take_notes or download_audio unless the user specifically requests visuals. Extract visual context from a video at moments that matter. Four modes: (1) Query mode (default): describe what you need visual context for and the tool finds those moments in the transcript, then extracts frames. (2) Auto mode: autonomously detects moments where the speaker implies visual content (UI actions, screen recordings, demonstrations). (3) Manual mode: extract frames at specific timestamps. (4) Assist mode: analyzes the transcript for visual gaps and returns time ranges where the user should provide their own screenshots (ideal for talking-head videos where the speaker describes a UI but doesn't show it). Frames are stored in augent memory alongside the transcription and embedded in the .md file as Obsidian wikilinks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoVideo URL (YouTube, etc). Downloads the video automatically if video_path is not provided.
autoNoAuto mode: autonomously detect visual moments from transcript (UI actions, demonstrations, spatial references). Default: false. Ignored if query or timestamps are provided.
clearNoRemove all previously extracted frames and the visual context .md for this video. Use to redo or clean up. Default: false
queryNoWhat you need visual context for. The tool searches the transcript semantically and extracts frames at matching moments. Examples: 'connecting Gmail to the agent', 'the dashboard configuration', 'where he sets up the branching logic'.
top_kNoNumber of transcript matches to extract frames for in query mode. Default: 10
assistNoAssist mode: analyze the transcript for visual gaps and return time ranges where the user should provide their own screenshots. Ideal for talking-head videos or podcasts where the speaker describes a workflow or UI but the video doesn't show it. No frames are extracted — instead, returns structured gaps with time windows, transcript excerpts, and what kind of screenshot would help. Default: false.
max_framesNoMaximum frames to extract. Default: 30
model_sizeNoWhisper model size for transcription. Default: 'tiny'
timestampsNoManual mode: list of timestamps (in seconds) to extract frames at. Overrides query and auto detection.
video_pathNoPath to a video file (MP4, MKV, etc). Can be output from clip_export.
context_wordsNoWords of context around each match in query mode. Default: 40
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 that frames are stored in augent memory, embedded as Obsidian wikilinks, and describes auto/manual/assist mode behaviors. It does not cover all edge cases (e.g., errors, download behavior), but provides substantial behavioral context.

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 slightly longer than necessary but well-structured, front-loading the critical usage restriction and then compactly describing modes. Each sentence contributes, though the mode enumeration could be more terse.

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?

Given the tool's complexity (11 params, no output schema), the description covers the core functional behavior, modes, and storage details. It does not detail the exact return format for query or assist outputs, but offers enough for an agent to invoke correctly in most scenarios.

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 description coverage is 100%, so the baseline is 3. The description adds value by explaining how the modes (query, auto, manual, assist) correspond to parameters like `query`, `auto`, `timestamps`, and `assist`. It also notes `video_path` can be output from `clip_export`, linking to a sibling tool.

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 the tool's purpose: extract visual context from videos at relevant moments. It is distinguished from sibling tools by explicitly limiting usage to visual requests and providing mode breakdowns (query, auto, manual, assist).

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

Usage Guidelines5/5

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

Provides explicit when-to-use ('ONLY call this tool when the user explicitly asks for visual context') and when-not-to ('Do NOT call it alongside take_notes or download_audio unless the user specifically requests visuals'). It also gives mode selection guidance, which is an alternative-aware usage guide.

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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