YouTube Transcript Downloader

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

Annotations already declare read-only and non-destructive, but the description adds meaningful behavioral context: requires an API key, credit charge per video including repeat calls, free skipping for videos without captions, typical cost for 10 videos, and a rate limit of 5 requests per 10 seconds. This goes beyond annotation defaults and enriches the agent's understanding of side effects and constraints.

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?

Every sentence earns its place: purpose, usage guidance, cost model, and rate limit are all packed into a few clear sentences. Information is front-loaded with the core function before operational details, with no filler or repetition.

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?

The description is fully self-contained for selecting and invoking the tool: it specifies output format, input scope, cost behavior, exclusions (videos without captions), rate limiting, and sibling alternatives. Output schema exists, so return-value detail is not required. It covers all essential operational and selection dimensions for a non-trivial tool.

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 already documents both parameters well (100% coverage), so baseline is 3. The description adds value by tying 'limit' to credit costs ('A 10-video call typically costs up to 10 credits, so start with a small limit') and providing example URL formats, giving practical semantics beyond the schema alone.

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 opens with a precise verb and object: 'Get transcripts for a YouTube channel's most recent videos (newest first) as timestamped markdown, one section per video.' It clearly distinguishes from siblings by specifying channel-level scope and explicitly contrasting with get_transcript for a single known video.

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?

Provides explicit usage context: 'Use for research across a creator's recent output' and names the alternative 'for one known video use get_transcript.' Also includes practical operational guidance on credits and rate limits, which helps the agent decide when and how to invoke it.

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?

Annotations already indicate read-only and non-destructive, but the description adds significant behavioral details: requires an API key, charges 1 credit per transcript retrieved including repeat calls, skips videos without captions, provides a cost estimate, and specifies rate limits. These are valuable beyond the structured annotations.

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 front-loaded with the main action and output format, followed by usage context, cost, and rate limit. Every sentence contributes unique information with no redundancy or fluff. It is 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 simple schema (2 params) and existing output schema, the description covers all necessary angles: purpose, ordering, output format, usage contexts, alternative tools, cost behavior, rate limits, and authentication. It is fully complete for an agent to select and invoke confidently.

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 coverage is 100% for both 'url' and 'limit', so the baseline is 3. The description adds practical context for the 'limit' parameter by explaining the credit charge per video and advising 'start with a small limit', which meaningfully enhances understanding of cost implications. This exceeds the schema's pure definitional 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 uses a specific verb ('Get transcripts') with a clear resource ('videos in a YouTube playlist') and output format ('timestamped markdown, one section per video'). It explicitly distinguishes itself from sibling tool get_transcript for single videos. This is unambiguous and well-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?

Provides explicit when-to-use guidance: 'Use for working through a course, series, or curated list' and directly names the alternative for single videos: 'for one known video use get_transcript'. This clearly communicates appropriate contexts and exclusions.

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?

Annotations already provide readOnlyHint and destructiveHint, but the description adds significant behavioral context: API key requirement, credit cost per call, error behavior for videos without captions, and a rate limit. This goes well beyond the structured annotations.

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?

Three sentences, no wasted words. The core purpose is front-loaded, followed by cost/rate-limit caveats and error behavior. Each sentence adds value.

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 single-parameter read-only tool with an output schema present, the description covers input, behavioral safety, cost, error conditions, and rate limits. There are no significant gaps given the structured fields.

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 single parameter 'video' is fully described in the schema (100% coverage), including accepted formats. The description does not add extra parameter-specific details, but the schema carries the semantic load, so a baseline score of 3 is appropriate.

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 uses a specific verb ('Get') with a clear resource ('full transcript of a single YouTube video') and output format ('timestamped markdown'). It distinguishes from siblings by emphasizing 'single video' vs channel/playlist tools.

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?

Provides clear context for when to use (single video) and practical guidance about credits and reusing transcripts. Does not explicitly name sibling alternatives for multi-video scenarios, but the 'single video' phrasing implicitly differentiates.

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?

Annotations already mark readOnly=true and destructive=false; the description reinforces with 'Read-only' and adds significant non-obvious behavior: API key requirement, credit billing (≈3 per 1,000 words, min 5, repeat calls billed), error cases that avoid charges, and a 5 req/10s rate limit. No contradiction with annotations.

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 compact yet information-dense; the first sentence states core purpose, the second gives usage guidance, and subsequent sentences handle cost, errors, and rate limits – all non-redundant. Length is justified by the billing and rate-limit caveats.

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 one documented parameter, an output schema, and readOnly annotations, the description covers all operational essentials: prerequisites (API key), cost model, failure conditions, and throttling. There are no major behavioral gaps.

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 coverage is 100% and the single 'video' parameter is well documented in the schema (ID or URL forms). The description adds no extra parameter-level meaning, so the baseline of 3 is appropriate.

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 opens with a specific verb and resource ('Get a cleaned-up transcript') and enumerates concrete transformations (punctuation, filler removal, paragraphs, name fixes). It explicitly distinguishes from get_transcript, making its purpose unmistakable.

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 states 'Use when raw captions are too messy to read or quote' and names the explicit alternative 'for a plain transcript use get_transcript'. It also lists error conditions (human-uploaded captions, over ~7,000 words), giving clear guidance on 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.