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u2n4

video-url-analyzer-mcp

by u2n4

analyze_video_segment

Analyze a selected video segment by specifying start and end times, using AI to process visual, audio, and textual content.

Instructions

Analyze only a selected video segment.

Uses Gemini video_metadata clipping when available. Gemini performs video/audio/visual reasoning. detail controls model + max_output_tokens + thinking/media config.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYes
urlYes
modelNo
startYes
detailNocompact
promptNoAnalyze this segment in detail.
return_full_textNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It reveals it uses Gemini clipping and reasoning, but omits critical traits like read-only status, rate limits, or side effects. This is insufficient for a complex analysis tool.

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 concise with four sentences, beginning with a clear purpose statement. However, it includes some redundancy (e.g., 'Gemini performs video/audio/visual reasoning' is implied by 'analyze'), slightly reducing efficiency.

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?

Given the tool's complexity (7 parameters, required start/end/url, output schema exists), the description is far too minimal. It lacks details on parameter formats, output behavior, and prerequisites, leaving significant gaps for effective agent usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate. It only explains the 'detail' parameter's role in controlling model and tokens, leaving six parameters (url, start, end, model, prompt, return_full_text) without any semantic addition. This poorly supports an agent's understanding.

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 analyzes a selected video segment, using a specific verb and resource. It distinguishes itself from siblings like 'analyze_video' by focusing on segments, providing specific purpose.

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

Usage Guidelines4/5

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

The description implies usage for video segments rather than full videos, offering clear context. However, it does not explicitly state when not to use it or list alternative tools, missing some guidance.

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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