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u2n4

video-url-analyzer-mcp

by u2n4

get_transcript

Extract speech transcripts from video URLs. YouTube returns immediately; for TikTok/Instagram poll with job ID. Slideshows without audio return structured response.

Instructions

Extract speech transcript from a video or slideshow audio track.

YouTube returns the result immediately. TikTok/Instagram return a job_id — use check_analysis_job(job_id) to poll for the result. Slideshows without audio return a structured slideshow_no_audio response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe video URL (YouTube, TikTok, Instagram, or other).
langNoLanguage hint (e.g., 'en', 'ar', 'auto'). Defaults to auto-detect.auto
modelNoGemini model to use. Defaults to gemini-3.5-flash.gemini-3.5-flash

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It does so by explaining the synchronous vs. asynchronous behavior, the need to poll for certain platforms, and a special response case for slideshows without audio. This is good transparency, though could mention error scenarios or rate limits.

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 extremely concise with three sentences, each adding distinct value: main purpose, platform-specific behaviors, and special case. It is front-loaded and every sentence earns its place.

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

Completeness4/5

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

Given the tool's complexity (3 parameters, output schema present, sibling tools), the description covers key contextual aspects: async handling, special response types, and platform differences. It is complete enough for correct invocation, though could include note about authentication or rate limits.

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 description coverage is 100%, so baseline is 3. The description adds minimal parameter meaning beyond the schema: it doesn't elaborate on url format, language hint specifics, or model selection. The param info is already sufficient from schema, but description doesn't enhance it.

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's purpose: extracting speech transcript from video/audio. It specifies the verb 'Extract' and resource 'speech transcript', and distinguishes from sibling tools by detailing platform-specific behaviors (YouTube immediate, TikTok/Instagram async, slideshows special response).

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 provides usage guidance by explaining the immediate vs. polling pattern based on platform, and references check_analysis_job for async results. It lacks explicit 'when not to use' or full alternative tools, but the context is clear enough for proper selection.

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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