YouTube Ultimate Toolkit MCP

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 well by specifying the output format (base64-encoded MP3), time range parameters, and duration limits (max 120 seconds). However, it doesn't mention important behavioral aspects like rate limits, authentication requirements, or what happens with invalid URLs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured with three concise sentences that each add value: the core functionality, parameter guidance, and use case context. There's no wasted language, and the most important information comes first.

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

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 provides adequate but not complete coverage. It explains the core functionality and use cases well, but doesn't address error conditions, authentication, or detailed behavioral constraints that would be helpful for an AI agent.

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

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

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds some context about time range parameters ('Use time range parameters to limit duration') and mentions the 120-second maximum, but doesn't provide additional semantic meaning beyond what's in the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Extract an audio clip'), resource ('from a YouTube video'), and output format ('base64-encoded MP3'), distinguishing it from sibling tools like get_comments or get_screenshot that handle different data types. It provides a complete picture of what the tool does beyond just the name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 ('Useful for analyzing speech, music, or audio when transcript isn't available'), which helps differentiate it from potential alternatives. However, it doesn't explicitly mention when NOT to use it or name specific alternative tools from the sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 adds valuable context beyond the input schema: it specifies the return data (comment author, text, like count, reply count), mentions an environment variable requirement (YOUTUBE_API_KEY), and hints at the tool's utility. However, it lacks details on error handling, rate limits, or pagination behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: it starts with the core purpose, then lists return values, prerequisites, and utility in three concise sentences. Every sentence adds value without redundancy, making it efficient and well-structured.

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

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 fairly complete. It covers purpose, return data, prerequisites, and utility, but lacks output format details (e.g., structure of returned comments) and error scenarios. Since there is no output schema, some gaps remain in fully describing the tool's behavior.

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

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

The input schema has 100% description coverage, providing clear details for all parameters (url, maxResults, order). The description does not add any parameter-specific semantics beyond what the schema already explains, such as format examples or usage tips. According to the rules, with high schema coverage (>80%), the baseline is 3 even without param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get top comments from a YouTube video.' It specifies the resource (YouTube video comments) and the verb (get), but does not explicitly differentiate from sibling tools like get_video or get_audio, which target different resources. This makes it clear but not fully sibling-distinctive.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage context: 'Useful for understanding audience reactions and discussion topics.' This suggests when to use it (for analyzing video engagement), but does not explicitly state when not to use it or name alternatives among sibling tools. No explicit exclusions or comparisons are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It describes the tool's purpose and output format but lacks behavioral details like rate limits, authentication requirements, error conditions, or pagination behavior. The mention of 'limit' parameter implies some constraint, but this is already covered in the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with zero waste. The first sentence states the purpose and output, the second provides usage context. Every word earns its place, and information is front-loaded appropriately.

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

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

For a read-only tool with no annotations and no output schema, the description adequately covers the basic purpose and usage context. However, it lacks details about return format structure, error handling, or performance characteristics that would be helpful given the absence of structured output documentation.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., URL format examples or limit usage context), meeting the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get information about a YouTube playlist') and resource ('YouTube playlist'), listing the exact data returned (video titles, durations, URLs). It distinguishes from sibling tools by focusing on playlist metadata rather than audio extraction, comments, screenshots, or video content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for usage ('Useful for understanding playlist contents before selecting specific videos to transcribe'), which implicitly suggests it's a preliminary step. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and discloses key behavioral traits: it specifies the return format ('Returns the image as base64'), a system requirement ('Requires ffmpeg to be installed'), and the action's purpose. However, it lacks details on error handling, rate limits, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by return format, requirement, and usage context in three concise sentences. Each sentence adds value without redundancy, making it efficiently structured and easy to parse.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete: it explains what the tool does, its output, and a key requirement. However, it could improve by mentioning potential errors or limitations, such as video availability or timestamp validity.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples for timestamp beyond the schema's description, but does not compensate unnecessarily, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Capture a screenshot'), resource ('from a YouTube video'), and scope ('at a specific timestamp'), distinguishing it from sibling tools like get_audio or get_video by focusing on visual capture rather than audio extraction or metadata retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('Useful for getting visual context from specific moments in a video'), but does not explicitly state when to use this tool versus alternatives like get_video for general video info or get_audio for audio extraction. No exclusions or prerequisites beyond ffmpeg are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 mentions advanced options like timestamps, time range filtering, search, and key segments extraction, which adds useful context beyond basic retrieval. However, it lacks details on potential limitations (e.g., rate limits, authentication needs, or error handling for invalid URLs), leaving some behavioral aspects unclear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with two sentences: the first states the core purpose and features, and the second provides usage guidelines. Every sentence adds value without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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

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

Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and key features, but lacks details on output format (e.g., structure of metadata/transcript) and potential behavioral constraints. With no output schema, some completeness gaps remain, though the description does a solid job overall.

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

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

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds marginal value by summarizing the advanced options (e.g., 'timestamps, time range filtering, search within transcript, and key segments extraction'), but it doesn't provide additional syntax or format details beyond what the schema specifies. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get a YouTube video's metadata and English transcript') and distinguishes it from sibling tools like get_audio, get_comments, get_playlist, and get_screenshot by focusing on video-specific content extraction. It explicitly mentions advanced options like timestamps, filtering, and key segments extraction, making the purpose highly specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Use this to summarize YouTube videos, find specific moments, or extract key takeaways.' This gives clear use cases and distinguishes it from alternatives like get_audio (for audio extraction) or get_comments (for comment analysis), helping the agent select the appropriate tool based on the task.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.