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Glama

Burst frames in time range

video_get_frame_burst
Read-onlyIdempotent

Extract a burst of frames from a short video window to analyze motion, UI transitions, or animations. Specify start and end times to capture a sequence of images for inspection.

Instructions

Extract N frames across a short time window for motion, UI transitions, or animations.

Prefer narrow windows (e.g. 0.5–3s) with 4–16 frames.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
sourceYesAbsolute local path, file:// URI, direct media URL, or platform URL (YouTube/TikTok/etc via yt-dlp)
end_secYes
start_secYes
inline_imagesNo
response_formatNoResponse text formatmarkdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds the window/frame-count guidance, which is heuristic rather than behavioral, and contributes little about internal behavior (e.g., fetching across openWorld sources or output handling). No contradiction with annotations exists, so a mid score is warranted.

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?

Two compact sentences with no filler: the first front-loads purpose and use cases, the second delivers tuning guidance. Every sentence earns its place; only slightly more specific parameter coverage would push it to a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and only 33% parameter coverage, the description carries heavy weight but leaves key gaps: it never explains what the response looks like in markdown vs json, the effect of inline_images, or how this tool differs from video_extract_frames. The window/frame heuristics help, but an agent still cannot predict the tool's output or its exact sibling boundary.

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 coverage is only 33%, below the 50% threshold, so the description must partially compensate. It does so indirectly: '4–16 frames' maps to count and '0.5–3s' maps to the start/end window, offering useful bounds. However, it never defines inline_images or response_format behavior, and the exact semantics of start_sec/end_sec are left to the schema.

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 description states a clear verb-resource-scope ('Extract N frames across a short time window') plus three use cases (motion, UI transitions, animations). It is distinguished implicitly from the single-frame sibling video_get_frame, though it does not explicitly differentiate itself from the similarly named video_extract_frames.

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 actionable heuristics ('Prefer narrow windows e.g. 0.5–3s with 4–16 frames') and implicit usage context via the listed use cases. However, it gives no explicit when-not-to-use advice and does not name or route to any alternative sibling tool, leaving selection among video_extract_frames and video_get_frame to inference.

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