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video_keyframe_scene_cut_detector

Detect video scene cuts via color histogram and motion delta, extracting representative keyframes without full re-encoding.

Instructions

Detects video scene cut transitions using color histogram and motion delta differences, extracting representative keyframes without full re-encoding. (0.040 USDC on Base L2)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYesInput parameters or JSON string payload for the tool execution
paymentSignatureNoBase L2 USDC micropayment signature or transaction hash for x402 settlement

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

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 burden of behavioral disclosure. It adds useful context about the method (color histogram and motion delta) and an important efficiency trait ('without full re-encoding'), plus a cost note. However, it does not disclose input requirements, output format, auth needs, or potential side effects, so it is only partially transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence that packs purpose, method, output, efficiency, and cost with no filler or repetition. It is front-loaded with the core function and remains scannable.

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?

The tool appears complex (video processing, keyframe extraction), yet the description does not explain how to supply the video (payload format), what the output will contain, or how the payment signature is used. Since there is no output schema and no annotations, the description is incomplete for an agent to call this tool correctly without guessing.

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 100%, so the baseline is 3 even though the description adds no parameter-specific detail. The main parameter 'payload' is generic and opaque; the description does not clarify what payload fields should contain for this specific video tool, but the schema's generic explanation prevents a lower score.

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 identifies the specific operation (detecting video scene cut transitions) and the resource (video keyframes), using a concrete verb and mechanism. It distinguishes itself from all sibling tools because no other listed tool covers video scene-cut or keyframe extraction.

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?

The usage is implicitly clear: use this tool when needing scene cut detection and keyframe extraction from video. However, it does not explicitly state when to prefer it over alternatives or provide any exclusion criteria, leaving the guidance somewhat implied rather than explicit.

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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