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get_frames

Read-onlyIdempotent

Retrieve keyframe images from a screen recording to see what was displayed at a specific moment or across a time range. Filter near-duplicates for a concise visual summary.

Instructions

Fetch stored keyframe images (JPEG, <=1568px wide) as MCP image content: the frames nearest to at_ms, OR unique frames across [start_ms, end_ms] evenly thinned to max_frames. Serves unique frames by default (near-duplicates from static scenes are filtered); hard cap 6 images per call. When NOT to use: exact instants between keyframes or native-resolution detail (use extract_frame), or finding on-screen text (use search — OCR text is indexed). Examples:

  • get_frames(job_id="...", at_ms=83500) — what was on screen when the remark at 1:23.5 was spoken

  • get_frames(job_id="...", at_ms=83500, max_frames=2) — tighter context, fewer tokens

  • get_frames(job_id="...", start_ms=0, end_ms=600000, max_frames=6) — overview strip of the first 10 min

  • get_frames(job_id="...", start_ms=290000, end_ms=310000, include_duplicates=true) — every capture near 5:00

  • transcript hit at t_ms=421500 → get_frames(job_id, at_ms=421500) for the visual evidence

  • walking a demo scene by scene → one ranged call per scene beats one giant range

  • frame files are named by video-ms (t00083500.jpg ↔ t_ms 83500) — stable refs for findings

  • keep max_frames at 2-4 unless you are truly comparing scenes; images are token-expensive

  • audio-only job → this tool errors by design; use get_transcript / get_moment instead

  • anti-example: need EXACTLY 12:34.500 between two keyframes → extract_frame(job_id, at_ms=754500)

  • anti-example: "find the screen with the red error banner" → search(job_id, "error") first, then jump

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
at_msNo
end_msNo
job_idYes
start_msNo
max_framesNo
include_duplicatesNo
Behavior5/5

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

The description adds significant behavioral context beyond annotations: image format/size, near-duplicate filtering, hard cap of 6, error on audio-only jobs, and stable file naming. No contradictions with annotations.

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?

Well-structured with examples and anti-examples, but slightly verbose. Every sentence adds value, though some redundancy in examples could be trimmed. Excellent front-loading of core behavior.

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 complexity (6 params, no output schema, multiple siblings), the description covers return value, constraints, edge cases, token costs, and integration patterns. Complete for an AI agent to use effectively.

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?

With 0% schema description coverage, the description fully explains each parameter's role through examples and notes: at_ms vs. start_ms/end_ms, max_frames behavior, include_duplicates effect, job_id obligatory. Compensates comprehensively.

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 fetches keyframe images, specifies the return type (JPEG MCP images), explains the two modes (at_ms or range with thinning), and distinguishes it from siblings like extract_frame and search.

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?

Explicitly describes when to use and when not to use, listing alternatives (extract_frame, search, get_transcript, get_moment). Includes examples covering common scenarios and anti-examples for incorrect usage.

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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