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extract_video_frames

Extract key frames from videos to create previews, thumbnails, or datasets. Choose uniform spacing, keyframe detection, or scene changes to capture representative moments.

Instructions

从视频中提取关键帧图像

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
strategyNo提取策略:uniform(均匀间隔), keyframe(关键帧), scene_change(场景变化)uniform
maxFramesNo最大提取帧数(默认10帧)
outputDirNo输出目录路径(可选)
videoPathYes视频文件路径

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.2

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only says frames are extracted. It does not mention output format, file naming, whether outputDir is created, overwrite behavior, dependencies, or side effects. The term '关键帧' may also mislead agents into thinking only keyframe strategy is supported, contradicting the default uniform strategy.

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 a single short sentence with no filler, which is efficient. However, the conciseness comes at the cost of omitting useful context like strategy behavior and output details, making it under-specified rather than optimally concise.

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?

For a tool with four parameters, an enum, and no output schema, the description is too thin. It does not explain how extracted frames are returned or saved, nor what each strategy produces. An agent would need to rely on parameter names and descriptions alone to understand the full workflow.

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?

The schema already describes all four parameters, including enum options for strategy, with 100% coverage, so the baseline is 3. The description adds no extra parameter semantics, such as path format requirements or how maxFrames interacts with strategy.

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?

Description clearly states the action — extracting keyframe images from a video — using a specific verb and resource. It is distinguishable from sibling tools like analyze_video_content and get_video_info, though it does not explicitly name alternatives. Minor ambiguity exists because '关键帧' overlaps with the 'keyframe' strategy value while the tool also supports uniform and scene_change extraction.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives such as analyze_video_content or get_video_info. It also does not explain when to choose uniform, keyframe, or scene_change strategies. Usage context must be inferred entirely from the tool name and schema.

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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