mcp-youtube-transcript

by of3y

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Server Quality Checklist

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Latest release: v0.5.0
Disambiguation2/5
Multiple tools (get_youtube_transcript, get_youtube_transcript_ytdlp, get_plain_text_transcript) perform nearly identical functions, differing only in extraction method or output format. This creates significant ambiguity for an agent trying to select the right tool.
Naming Consistency3/5
Most tools follow a get_* prefix, but the presence of list_, load_, create_, and the _v2 suffix breaks the pattern. The names are readable and descriptive, but not uniformly consistent.
Tool Count4/5
8 tools is within the typical well-scoped range for a domain-specific server. However, the three transcript retrieval tools are somewhat redundant and could be consolidated without losing functionality.
Completeness4/5
The server covers the full transcript workflow: retrieval, plain text extraction, metadata, quality analysis, and resource management. Minor gaps like timestamp filtering or playlist support exist but do not break core usage.
Average 3.5/5 across 8 of 8 tools scored. Lowest: 2.9/5.
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
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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?
There are no annotations, so the description carries the full burden of behavioral disclosure. The only behavioral claim is 'enhanced extraction pipeline,' which hints at a more involved process but does not explain potential side effects, rate limits, failure modes, or whether it requires authentication. As a 'get' operation, it is presumably safe, but the description lacks any explicit transparency about what happens under the hood.
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, consisting of a single line and an 'Args' section, with no redundant filler. It is front-loaded with the core purpose. The 'Args' section adds useful parameter context, though it is slightly under-specified for a tool with no schema descriptions. Overall, it earns a high score for brevity, though not a 5 due to the lack of behavioral or usage detail.
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?
The tool has no output schema, so the description should explain what metadata is returned. Saying 'comprehensive video metadata' is too vague to guide an agent on expected return values. It also lacks any context about when this tool is relevant or how it differs from simpler metadata retrieval. Given the simple 1-parameter schema, the description is incomplete and leaves significant ambiguity.
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 0%, so the description must compensate. The description adds the crucial context that video_url is a 'YouTube video URL,' which the schema does not provide. However, it does not elaborate on URL format, optional parameters, or any constraints, so it only minimally meets the compensation requirement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get comprehensive video metadata using enhanced extraction pipeline.' It specifies a clear verb (Get) and resource (video metadata) and is distinguishable from sibling tools which all relate to transcripts. However, the term 'comprehensive' is vague and does not specify exactly what metadata fields are included, preventing a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The sibling tools are all transcript-related, so the implicit context is that this tool is for metadata, but no explicit use cases, prerequisites, or alternatives are mentioned. The description does not explain when this tool is preferred over a simpler metadata tool or any other scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, yet it only says 'comprehensive quality analysis' without detailing what the analysis includes, whether it is read-only, or what output format to expect. The absence of any such context is a significant gap.
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 appropriately short, with a clear 'Args' structure that front-loads the key parameter. The 'Args' block slightly duplicates the input schema but does not harm readability, and every sentence earns its place in stating the core purpose.
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 that there is no output schema and no annotations, the description should explain what 'quality analysis' entails and what the response includes, but it does not. This is especially inadequate for a tool that presumably returns a complex analysis of a transcript, and the description offers no hints about return structure or side effects.
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?
The description adds 'YouTube video URL' as clarification for the single parameter, which is marginally more specific than the schema's 'Video Url' title. However, with 0% schema description coverage, this minimal addition is insufficient to fully clarify parameter semantics, especially since no validation or format expectations are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Get comprehensive quality analysis') and a resource ('YouTube video transcript'), which differentiates it from sibling tools focused on retrieval or metadata extraction. However, 'quality analysis' is somewhat broad and could be more specific about what aspects are analyzed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus its siblings (e.g., get_youtube_transcript or get_enhanced_video_metadata). There is no mention of prerequisites, use cases, or exclusions, leaving the agent to infer the tool's purpose from its name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must convey behavioral traits, but it only says 'load' without disclosing whether this is a safe read, whether it requires authentication, what happens on missing video_id, or any side effects. The absence of such details leaves the agent guessing.
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 one clear sentence followed by a simple arg list. Every word earns its place, and there is no redundancy or filler.
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 simplicity (one parameter, no output schema), the description is still incomplete. It does not mention what the loaded resource looks like, potential errors, or how it relates to sibling tools. The term 'resource' is undefined, leaving important gaps for an agent to invoke correctly.
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?
The schema has 0% description coverage, and the description does add meaning by stating 'video_id: YouTube video ID to load transcript for,' clarifying the parameter's role. However, it is minimal and does not explain any constraints or formats beyond the identifier.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'load' and identifies a clear resource ('transcript resource') and key identifier ('video ID'), making the primary function understandable. However, it does not explicitly distinguish itself from sibling tools like get_youtube_transcript, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus the alternatives (e.g., get_youtube_transcript, list_transcript_resources). The description merely states what it does without context on prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention output format, error handling, rate limits, or what happens if extraction fails. The only behavioral hint is 'primary extraction method,' which is not elaborated.
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 a single purpose sentence and a simple Args list. Every word is functional, and the structure is easy to parse. No filler or redundant content.
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 presence of sibling tools and no output schema or annotations, the description is incomplete. It does not specify the return format, when to use alternative extraction methods, or any constraints. An agent would struggle to decide between this and the ytdlp variant or understand what to expect as a result.
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?
The description adds some semantics beyond the schema by labeling video_url as 'YouTube video URL' and providing examples for language codes ('en', 'es'). However, it does not explain accepted formats, fallback behaviors, or edge cases, so it only partially compensates for the 0% schema description coverage.
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 action (Get the transcript), the resource (YouTube video), and the specific method (primary extraction method), which distinguishes it from the sibling tool get_youtube_transcript_ytdlp. The verb and resource are explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus its alternatives. The phrase 'primary extraction method' implies it is the default but does not explain when to choose ytdlp, plain text, or other variants. No exclusions or conditional advice are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure, yet it only states the method (yt-dlp) and parameter syntax. It does not mention potential failures (e.g., no transcript available), return format, rate limits, or authentication requirements, leaving the agent without critical operational 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 extremely concise, leading with the purpose and then directly listing arguments. No wasted words; every sentence earns its place, and the structure makes the tool easy to parse quickly.
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 absence of an output schema and the presence of multiple transcript-related siblings, the description lacks essential context about return format and when to choose this tool over others. The simplicity of the tool does not justify the missing guidance on expected output and selection criteria.
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 descriptions are absent (0% coverage), so the description must compensate. It does list both parameters, clarifies that video_url is a YouTube URL, and adds examples and a default for language, providing meaningful semantic value beyond the raw schema.
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 action (get transcript), resource (YouTube video), and method (yt-dlp). The phrase 'most reliable method' distinguishes it from sibling tools like get_youtube_transcript, making the purpose unambiguous and differentiated.
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?
'Most reliable method' provides clear context for preferring this tool, but there is no explicit when-not or alternative naming. The absence of exclusionary guidance prevents a 5, but the comparative statement offers useful direction.
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 the full burden. It discloses that the output is plain text without timestamps and explains the aggressive_dedup parameter's effect. However, it does not mention error handling, authentication, rate limits, or the exact return type, which would be helpful for a tool with no annotations.
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 concise and well-structured: a one-sentence summary followed by an Args section. Every line adds value, with no redundancy or extraneous detail.
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 description covers the core functionality and parameter semantics, but lacks details about output format (e.g., whether it returns a string), error cases, or how it relates to the sibling transcript tools. Given the absence of an output schema and annotations, it leaves some gaps.
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?
With 0% schema description coverage, the description compensates by defining both parameters: video_url as the YouTube video URL and aggressive_dedup as a flag for cleaner text. The explanations are clear and add meaning beyond the schema's bare titles and default value, though 'aggressive' could be more explicit.
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 function: extracting a clean plain text transcript without timestamps, optimized for AI analysis. The verb+resource (Extract + plain text transcript) is specific, and the 'without timestamps' detail differentiates it from likely sibling transcript tools.
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?
The description implies usage for AI analysis when a clean transcript without timestamps is needed. However, it does not explicitly mention when to use this tool over sibling tools like get_youtube_transcript or get_youtube_transcript_ytdlp, nor does it state any exclusions.
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 present, and the description notes that resources are stored transcript files loadable without tokens, which is a useful extra behavior. However, it does not disclose the return format, pagination, read-only nature, or authorization requirements, leaving gaps for a tool with no output schema.
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?
Two focused sentences front-load the primary purpose and add one supporting detail about token-free loading. No filler or redundancy.
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?
For a simple zero-param list operation, the description is workable, but it omits what the returned resource entries look like or how they relate to load_transcript_resource. Given no output schema and no sibling differentiation, a little more detail on the result format or next-step use would be needed for higher completeness.
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?
The input schema has zero properties, so there are no parameters or defaults for the description to clarify. Baseline for zero-parameter tools is 4, and the description's mention of 'all available' reinforces the query scope, although it adds no parameter-specific detail.
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 opens with a specific verb and resource: 'List all available transcript resources in the system.' This clearly distinguishes the listing tool from sibling tools focused on getting or loading transcripts.
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?
The description provides some implied context—'stored transcript files that can be loaded without tokens'—but never explicitly contrasts this with sibling tools like get_youtube_transcript or load_transcript_resource. It gives no when/when-not guidance, so an agent must infer that listing is intended for discovery before loading.
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 the full burden. It discloses the side effect of creating a markdown file at a specific path and the benefit of zero token consumption. However, it does not mention whether an existing resource with the same name is overwritten, what permissions are required, or what the tool returns after creation.
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 brief and well-structured: a purpose statement, a benefit/location sentence, and an Args list. Every sentence contributes value, and the information is front-loaded. No fluff or redundancy.
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 a mutation with no annotations and no output schema, so the description must cover side effects and results. It states the file creation path and persistence, but it does not explain the return value (e.g., resource URI or path) or error scenarios. For a simple create tool, this is a noticeable gap.
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?
The schema properties have no descriptions (0% coverage), but the description includes an Args section that explains both parameters: video_url is a YouTube video URL, and resource_name is optional with auto-generation if empty. This provides meaning beyond the bare schema, though it could add constraints like URL format or naming rules.
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 function: 'Create a persistent MCP resource from a YouTube video transcript.' It specifies the exact output (a markdown file in resources/transcripts/) and distinguishes it from siblings like get_youtube_transcript by focusing on persistence and zero-token access.
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 clear context for when to use this tool: when you want a persistent, zero-token-accessible resource from a transcript. It does not explicitly name alternatives or exclusions (e.g., 'if you don't need persistence, use get_youtube_transcript'), but the value proposition is evident.
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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