YouTube MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: transcript languages, comments, video metadata, transcript, and search. No overlap or ambiguity.
Naming Consistency5/5All tools follow a consistent verb_noun pattern ('get_*' for data retrieval, 'search_youtube' for search). The pattern is uniform and predictable.
Tool Count5/55 tools is well-scoped for a YouTube MCP server. Each tool covers a core operation without unnecessary bloat or deficiency.
Completeness5/5The tool set covers the essential read operations for YouTube video data: search, metadata, comments, and transcripts with language discovery. No obvious gaps for the stated purpose.
Average 4/5 across 5 of 5 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.
This repository includes a README.md file.
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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, description carries full burden. Covers sorting options and error case (disabled comments), but omits authentication needs, rate limits, or response structure details beyond basic fields. Incomplete for an unaided tool.
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?
Extremely concise: two sentences covering purpose, output, sorting, and error. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so return structure is not required. However, description lacks guidance on pagination strategy or how to handle multiple pages. Usage context is minimal given four parameters and a pagination mechanism.
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 coverage is 100%, so baseline 3. Description mentions sorting and error but adds little beyond schema descriptions (e.g., schema already says maxResults=20 per page). No new parameter insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool retrieves YouTube video comments with replies, listing returned fields (text, author, likes, date). Distinct from sibling tools like get_video_info, get_video_transcript, or search_youtube.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies use when you want comments, but no explicit when-to-use or when-not-to-use. No mention of alternatives (e.g., use search_youtube for finding videos). Lacks exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It reveals it returns both timestamped and plain text versions, supports auto-generated and manual captions, and errors if captions are unavailable. This is good coverage, though it doesn't mention rate limits or auth requirements.
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?
Two concise sentences, no fluff, front-loaded with primary purpose. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description need not detail return values. It covers key aspects: source (YouTube video), type (transcript/captions), and error case. Lacks mention of pagination support but that is handled by parameter descriptions.
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 coverage is 100%, so the schema already documents all parameters. Description adds minimal extra meaning beyond what schema provides (e.g., language code examples). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it gets YouTube video transcript/captions with timestamps. It distinguishes from sibling tools like get_transcript_languages (which lists languages) and get_video_comments (which gets comments).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description mentions language code specification and error behavior but does not explicitly guide when to use this tool vs alternatives like get_transcript_languages for listing available languages. Usage context is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It discloses return fields and limits (up to 50 results) and filtering options, but does not mention authentication, rate limits, or error handling. This is adequate but not comprehensive.
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 two sentences: first states purpose and output, second lists filters and sorting. No redundant information. Every sentence serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description does not need to detail return values extensively. It covers the main functionality: querying, filtering, sorting, and result limits. Missing details like pagination or language filters are acceptable given the schema coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by summarizing the return fields (title, channel, views, duration, URL) and the purpose of filters, which goes beyond the schema's parameter descriptions. However, it does not add detail per parameter beyond restating options.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search YouTube videos by query', which is a specific verb and resource. It distinguishes itself from sibling tools like get_video_transcript or get_video_comments, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly contrast with sibling tools. While the sibling list implies this tool is for searching, no guidance is given on when to use search_youtube versus other tools like get_video_info. The usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It explains the tool returns metadata fields and an error message for unavailable videos. However, it does not mention authentication requirements, rate limits, or whether the tool is read-only. Since it implies a read operation without explicit safety guarantees, a 3 is appropriate.
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?
Two sentences convey purpose, input, and error behavior without redundancy. The most critical information is front-loaded. Every sentence serves a clear function, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, has output schema), the description comprehensively covers input format, returned metadata fields, and possible error scenarios. The presence of an output schema means return value details are omitted appropriately. No gaps remain for a basic metadata retrieval tool.
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 coverage is 100% for the single 'video' parameter, with description already stating 'YouTube URL or video ID.' The tool description repeats this exact information without adding new details like allowed URL formats or examples. Thus, description adds no extra value beyond the schema, meeting the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get YouTube video metadata' and lists specific fields (title, views, likes, comment count, upload date, duration, tags, and description). This verb+resource+scope approach distinguishes it from sibling tools like get_video_transcript or search_youtube, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explicitly specifies input format: 'YouTube URL or video ID.' It also notes error conditions (private, deleted, unavailable). While it doesn't directly compare to siblings, the sibling names make usage context clear, and the input guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description covers return values (language codes and names) and scope (both manual and auto-generated). Lacks discussion of authentication or rate limits, but these are reasonable defaults for a listing tool.
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?
Two sentences: first states main purpose, second adds detail and usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists to document return format. Description fully covers purpose, usage, and behavioral aspects for this simple discovery tool.
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 coverage is 100% with a single parameter already described as 'YouTube video URL or video ID'. Description adds no new semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists available caption/transcript languages for a YouTube video, specifying both manual and auto-generated. It distinguishes itself from sibling tools like get_video_transcript and get_video_info.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to call this first before fetching a transcript, providing clear when-to-use guidance and implying when not to use it.
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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