YouTube MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose focused on retrieving transcripts from YouTube.
Naming Consistency5/5A single tool inherently has perfect naming consistency as there are no other tools to compare it against. The tool name 'get_transcript' follows a clear verb_noun pattern.
Tool Count2/5One tool is too few for a server named 'YouTube MCP Server', which suggests a broader scope. A single transcript retrieval tool feels incomplete for typical YouTube operations like searching videos, listing playlists, or managing uploads.
Completeness1/5The tool surface is severely incomplete for a YouTube server. It only covers transcript retrieval, with no tools for core YouTube functionalities such as video search, metadata retrieval, playlist management, or user interactions, leaving significant gaps.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions retrieving transcripts but doesn't describe what happens if no transcript exists, rate limits, authentication requirements, error conditions, or the format/scope of returned data. This leaves significant behavioral gaps for a tool that interacts with external APIs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Japanese that clearly communicates the core functionality without any wasted words. It's appropriately sized and front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and interaction with an external service (YouTube), the description is insufficient. It doesn't explain what format the transcript returns, error handling, availability constraints, or any behavioral aspects needed for reliable use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (url and lang with their descriptions and default). The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('取得します' - get/retrieve) and resource ('YouTubeの字幕' - YouTube subtitles/transcripts) with the input source specified ('URLまたは動画IDから' - from URL or video ID). It's specific about what the tool does, though it doesn't need to distinguish from siblings since there are none.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or limitations. It simply states what the tool does without context about appropriate use cases or constraints.
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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