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

With no annotations, the description carries the full burden but only states 'Returns: Formatted key points'. It does not disclose any behavioral details such as whether captions are required, error behavior, or limitations on num_points, so the agent lacks transparency about the tool's 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 concise and well-structured with Args and Returns sections. It is front-loaded with the purpose, but the structure could be slightly improved by integrating parameter descriptions into the schema, though overall it is efficient.

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 simplicity and the presence of an output schema for return values, the description is mostly complete. However, it lacks usage context relative to sibling tools and fails to mention any prerequisites or edge cases, which is a gap for an AI agent deciding when to invoke this 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?

Although schema description coverage is 0%, the description compensates by clearly explaining each parameter: video_url as 'YouTube URL or video ID', num_points as 'Number of key points to extract' with default, and language as 'Language code' with default. This adds meaningful semantic context beyond the raw schema.

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 'Extract' with a clear resource 'key points from a YouTube video', and this distinguishes it from siblings like get_transcript, summarize_video, and get_video_metadata, 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.

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 the sibling tools. The description only lists arguments and return value without specifying the ideal use case or exclusions, leaving the agent to infer usage.

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. It only states the return value ('Transcript text') and does not disclose behaviors like language fallback, unavailability errors, rate limits, or whether transcripts are auto-generated. Minimal output detail but no meaningful behavioral context.

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: one clear purpose line, a compact Args block, and a Returns line. Every sentence serves a purpose, and the format is scannable and front-loaded.

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 tool is simple, and the description covers both parameters and a basic return type. However, it omits important context such as error handling when a transcript is unavailable, whether the language parameter is strict or fallback, and any usage caveats. The output schema reduces the need to explain returns, but behavioral/contextual gaps remain.

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 0%, but the description compensates by explaining both parameters: video_url accepts 'YouTube URL or video ID', and language is a 'Language code (default: en)'. This adds practical meaning beyond the raw schema, though it lacks details on supported language formats or fallback behavior.

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 ('Fetch') and resource ('transcript of a YouTube video'), which clearly distinguishes it from sibling tools like summarize_video, extract_key_points, and get_video_metadata. It unambiguously states what the tool does in one sentence.

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?

Usage is implied: if you need a transcript, use this tool. However, it does not explicitly say when to prefer it over alternatives, nor does it mention any exclusions or prerequisites. The purpose sentence provides implied guidance but no explicit comparison to siblings.

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 must convey behavioral traits. It only states purpose and return value, without explicitly mentioning that the operation is read-only, potential errors, or network requirements. This is minimal disclosure for a tool with no annotation support.

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 highly concise and well-organized with clear Args and Returns sections. Every sentence adds value, and there is no redundant or filler content.

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 simplicity of the tool (one parameter) and the existence of an output schema, the description adequately covers purpose, parameter format, and return type. It may omit some edge-case behavior, but overall it is sufficiently complete for its low complexity.

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 provides no description for video_url, but the description adds that it accepts a YouTube URL or video ID. Since schema coverage is 0%, this compensation is essential and well-executed, though it could have been more detailed.

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 retrieves YouTube video metadata (title, channel, etc.), using a specific verb and resource. It is well-differentiated from the sibling tools, which handle transcripts, summaries, and key points.

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 a clear context for use (when you need video metadata) but does not explicitly mention alternatives or exclusions. It implies the correct scenario but lacks direct comparison to 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, the description carries the full burden of behavioral disclosure. It mentions input arguments and a return type, but does not disclose any limitations, prerequisites, network/API behavior, authentication needs, or potential errors. This is insufficient for a tool that likely fetches video data externally.

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 extremely concise and well-structured, with a one-sentence purpose followed by a clean Args and Returns block. Every line adds value, with no filler or redundancy.

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 relatively simple read-style tool, the description covers the core purpose, arguments, and return value, and the presence of an output schema covers return format details. However, it lacks usage guidance and behavioral caveats, which would round out its completeness.

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 has no descriptions for parameters, so the description compensates by explaining that video_url accepts a URL or ID, summary_length takes short/medium/long, and language defaults to en. This adds meaningful context beyond the schema, but it does not quantify what different summary lengths produce.

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 function with a specific verb+resource: 'Generate a summary of a YouTube video.' This distinguishes it in general from sibling tools like get_transcript or extract_key_points, but it does not explicitly differentiate or reference those alternatives, so it falls short of a 5.

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?

There is no guidance on when to use this tool versus its siblings (get_transcript, extract_key_points, get_video_metadata). The description only states what it does and lists parameters; no context about appropriate scenarios or exclusions is provided.

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