YouTube Transcript DL MCP Server

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity: clear_cache handles cache management, format_transcript processes existing data, get_bulk_transcripts extracts from multiple videos, get_cache_stats provides metrics, get_playlist_transcripts targets playlists, get_transcript handles single videos, and list_transcripts enumerates available transcripts. The descriptions clearly differentiate between single-video, multi-video, playlist, cache, and formatting operations.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case throughout: clear_cache, format_transcript, get_bulk_transcripts, get_cache_stats, get_playlist_transcripts, get_transcript, and list_transcripts. The naming is predictable and readable, using verbs like 'clear', 'format', 'get', and 'list' consistently across the set.
Tool Count5/5
With 7 tools, the count is well-scoped for a YouTube transcript server, covering core operations like extraction (single, bulk, playlist), caching, formatting, and listing without bloat. Each tool earns its place by addressing a specific aspect of transcript handling, making the set neither too thin nor too heavy for the domain.
Completeness4/5
The tool surface is nearly complete for transcript extraction and management, covering key operations: extraction (single, bulk, playlist), cache management (clear, stats), formatting, and listing. A minor gap exists in update or delete operations for transcripts, but agents can work around this, and the core workflows are well-covered for the server's purpose.
Average 3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 but only states the basic operation. It doesn't mention whether this is a read-only operation, potential rate limits, authentication requirements, error handling (e.g., for invalid video IDs), or what happens when transcripts are unavailable. For a bulk operation tool with zero annotation coverage, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with clear parameters documented elsewhere and follows good front-loading principles.
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?
For a bulk operation tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the return format, error conditions, performance characteristics, or how results are structured for multiple videos. The agent would need to guess about important behavioral aspects.
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 doesn't add any parameter-specific information beyond what's already in the schema (which has 100% coverage). It doesn't explain relationships between parameters, provide examples, or clarify semantics like what 'includeMetadata' actually includes. With high schema coverage, the baseline is 3, but no additional value is added.
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 action ('Extract transcripts') and resource ('from multiple YouTube videos'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_transcript' (single video) or 'get_playlist_transcripts' (playlist-based), which would be needed for a perfect score.
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 provides no guidance on when to use this tool versus alternatives like 'get_transcript' (single video) or 'get_playlist_transcripts' (playlist-based). It also doesn't mention prerequisites, rate limits, or error conditions, leaving the agent with insufficient context for optimal tool selection.
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 full burden for behavioral disclosure but offers minimal information. It states what the tool does ('Extract transcripts') but doesn't describe how it behaves - no mention of rate limits, authentication requirements, error handling, processing time, or what happens with invalid playlist IDs. For a tool that likely makes external API calls, this is inadequate.
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 a single, focused sentence that efficiently communicates the core functionality without unnecessary words. It's front-loaded with the main action and resource, making it immediately scannable and easy to understand at a glance.
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?
For a tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the output looks like (especially important with format options), doesn't mention error conditions or limitations, and provides no context about the extraction process. The agent would need to guess about many behavioral aspects.
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 all parameters are documented in the schema itself. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain what 'includeMetadata' actually includes or provide examples of playlist ID formats). This meets the baseline for high schema coverage but doesn't enhance understanding.
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 action ('Extract transcripts') and target resource ('from all videos in a YouTube playlist'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_transcript' (single video) or 'get_bulk_transcripts' (multiple videos), which would require more specific scoping language to achieve a perfect score.
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 provides no guidance on when to use this tool versus alternatives like 'get_transcript' for single videos or 'get_bulk_transcripts' for multiple videos without playlist context. There's no mention of prerequisites, limitations, or typical use cases, leaving the agent to infer usage from the tool 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 carries the full burden of behavioral disclosure. It states the action ('Extract') but doesn't describe what happens if the video lacks a transcript, rate limits, authentication needs, error handling, or the structure of the output. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity, making it 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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is incomplete. It lacks information on output format details, error cases, or behavioral traits. Without annotations or an output schema, the description should do more to compensate, such as explaining what the extracted transcript looks like or common pitfalls.
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 no parameter semantics beyond what the input schema provides. Since schema description coverage is 100%, the schema already documents videoId, language, and format with descriptions and defaults. The baseline score of 3 is appropriate as the schema does the heavy lifting, but the description doesn't enhance understanding of parameter usage.
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 purpose with a specific verb ('Extract') and resource ('transcript from a single YouTube video'), distinguishing it from siblings like get_bulk_transcripts (multiple videos) and get_playlist_transcripts (playlist). However, it doesn't explicitly differentiate from format_transcript, which might be for post-processing rather than extraction.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose get_transcript over get_bulk_transcripts for multiple videos, or when format_transcript might be needed for processing. There's no context about prerequisites or exclusions.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('List all available transcripts') but doesn't describe what 'available' means (e.g., language options, format types), whether there are rate limits, authentication needs, or how results are returned (e.g., pagination, error handling). This leaves significant gaps for a tool with no annotation coverage.
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 a single, clear sentence that directly states the tool's purpose without any wasted words. It is front-loaded and efficiently communicates the core functionality, making it 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'available transcripts' entails (e.g., multiple languages, formats), behavioral traits like rate limits, or how to interpret results. For a tool that likely returns a list of transcripts, more context is needed to guide effective use.
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%, with the single parameter 'videoId' documented as 'YouTube video ID or URL'. The description adds no additional meaning beyond this, such as examples of valid IDs or URL formats. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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 verb ('List') and resource ('all available transcripts for a YouTube video'), making the purpose immediately understandable. However, it doesn't distinguish this tool from sibling tools like 'get_transcript' or 'get_bulk_transcripts', which likely have overlapping functionality but different scopes or outputs.
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 provides no guidance on when to use this tool versus alternatives such as 'get_transcript' (which might fetch a single transcript) or 'get_bulk_transcripts' (which could handle multiple videos). It lacks explicit when/when-not instructions or prerequisites, leaving usage context implied at best.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Clear' implies a destructive mutation, but the description doesn't specify whether this is reversible, what permissions are needed, or what happens to system performance. It lacks details on rate limits, side effects, or return values, which is a significant gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded and directly states the tool's purpose, making it highly concise and well-structured for quick understanding.
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 complexity of a destructive cache-clearing operation with no annotations and no output schema, the description is incomplete. It doesn't explain behavioral aspects like safety, permissions, or what to expect after execution, which are critical for such a tool.
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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, earning a baseline score of 4 for tools with no parameters.
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 'Clear the transcript cache' clearly states the action (clear) and target resource (transcript cache). It uses a specific verb and identifies the resource, though it doesn't explicitly differentiate from sibling tools like 'get_cache_stats' or 'list_transcripts' beyond the action type.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, consequences, or relationships to sibling tools like 'get_cache_stats' for monitoring or 'list_transcripts' for accessing cached data, leaving usage context unclear.
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?
No annotations are provided, so the description carries full burden. It states the tool formats data but doesn't disclose behavioral traits such as whether it's read-only, if it modifies input data, performance characteristics, or error handling. This is a significant gap for a tool with no annotation coverage.
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 a single, efficient sentence that front-loads the core purpose without waste. It's appropriately sized for the tool's complexity, with every word earning its place.
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?
Given no annotations, no output schema, and 2 parameters with full schema coverage, the description is minimally adequate. It states the purpose but lacks behavioral context and output details, making it incomplete for a formatting tool that might have side effects or specific return formats.
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%, with both parameters well-documented in the schema. The description adds no additional meaning beyond the schema, such as explaining format details or transcript structure. Baseline 3 is appropriate as the schema does the heavy lifting.
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 verb 'format' and the resource 'existing transcript data', specifying it transforms data into different formats. It distinguishes from siblings like 'get_transcript' or 'list_transcripts' by focusing on formatting rather than retrieval, though it doesn't explicitly name alternatives for differentiation.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing transcript data first), exclusions, or compare with siblings like 'get_bulk_transcripts' for bulk operations. Usage is implied but not specified.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read operation, the description doesn't specify whether this requires authentication, what format the statistics are returned in, or if there are any rate limits. This leaves significant gaps for a tool with zero annotation coverage.
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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, making it 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 that there are no annotations and no output schema, the description is incomplete. It doesn't explain what 'cache statistics and performance metrics' include, how they're formatted, or any behavioral aspects like error handling. For a tool with zero structured metadata, this minimal description leaves too many questions unanswered.
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 tool has 0 parameters, and the schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for this scenario is 4, as the description appropriately doesn't waste space on non-existent parameters.
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 action ('Get') and the resource ('cache statistics and performance metrics'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling tools (like clear_cache or format_transcript), which would be needed for a perfect score.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.
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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