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dEitY719

YouTube Summary MCP Server

by dEitY719

get_transcript

Fetch the transcript of a YouTube video by providing its URL or ID. Optionally specify a language code to receive the transcript in your preferred language.

Instructions

Fetch the transcript of a YouTube video.

Args: video_url: YouTube URL or video ID language: Language code (default: en)

Returns: Transcript text

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoen
video_urlYes

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 carries the full burden. It only states the return value ('Transcript text') and does not disclose behaviors like language fallback, unavailability errors, rate limits, or whether transcripts are auto-generated. Minimal output detail but no meaningful behavioral context.

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 appropriately sized: one clear purpose line, a compact Args block, and a Returns line. Every sentence serves a purpose, and the format is scannable and front-loaded.

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

Completeness3/5

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

The tool is simple, and the description covers both parameters and a basic return type. However, it omits important context such as error handling when a transcript is unavailable, whether the language parameter is strict or fallback, and any usage caveats. The output schema reduces the need to explain returns, but behavioral/contextual gaps remain.

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

Parameters4/5

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

Schema description coverage is 0%, but the description compensates by explaining both parameters: video_url accepts 'YouTube URL or video ID', and language is a 'Language code (default: en)'. This adds practical meaning beyond the raw schema, though it lacks details on supported language formats or fallback behavior.

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 uses a specific verb ('Fetch') and resource ('transcript of a YouTube video'), which clearly distinguishes it from sibling tools like summarize_video, extract_key_points, and get_video_metadata. It unambiguously states what the tool does in one sentence.

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?

Usage is implied: if you need a transcript, use this tool. However, it does not explicitly say when to prefer it over alternatives, nor does it mention any exclusions or prerequisites. The purpose sentence provides implied guidance but no explicit comparison to siblings.

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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