Tubask

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

With no annotations, the description carries full responsibility for disclosing behavior. It meaningfully describes the output structure (segments[], display_timestamp, end_seconds, match_count, language metadata, next_steps[]), which goes beyond the input schema and gives the agent a clear picture of what to expect. It stops short of documenting potential edge cases (e.g., no captions available, rate limits), but for a read-only fetch tool, the transparency is reasonably strong.

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 exactly two sentences: the first states the core purpose and capabilities, the second outlines the JSON return structure. Every sentence earns its place, the most critical information is front-loaded, and there is no redundancy or filler. It is ideal in size and structure.

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 8 parameters and no output schema, the description does a solid job of conveying the essential context: what the tool does, the modes of operation, the key returned fields, and the alternative for structured overviews. It is not exhaustive—for instance, it doesn't demonstrate how parameters combine—but it is sufficient for an agent to correctly select and invoke the tool for typical use cases.

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 baseline is 3. The description adds light narrative context by summarizing modes ('full text, keyword search, time range, or paginated segments'), but each parameter is already thoroughly explained in the schema. The description does not add significant meaning beyond what the schema already provides, so no higher score is warranted.

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: 'Fetch YouTube captions for one video' with specific modes (full text, keyword search, time range, paginated segments). It distinguishes from the sibling summarize_video by explicitly directing structured overviews there, making the tool's specific purpose unambiguous.

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 usage context by enumerating the types of transcript tasks it supports and explicitly notes that 'Structured overview of a video uses summarize_video,' which serves as a when-not/alternative directive. This is sufficient guidance for an agent to decide between get_transcript and summarize_video, and implicitly differentiates from youtube_query by focusing on caption retrieval.

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 itself discloses the JSON output structure, the dependency on captions (including auto-generated), and transcript sampling via coverage{}. It stops short of explaining failure modes when captions are unavailable, so not a full 5.

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?

Four sentences, each purpose-driven: statement of function, output shape, prerequisite, and when to use an alternative. No 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?

The description covers function, output, prerequisite, and sibling differentiation. For a tool without an output schema, the return-field list adds necessary context, making it self-sufficient.

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% with meaningful parameter descriptions, so the description doesn't need to add param details. It does tie the tool's operation to captions, which is broader context rather than per-param semantics.

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-resource pairing ('Summarize one YouTube video') and explicitly distinguishes itself from youtube_query for multi-video synthesis. Listing the return fields further clarifies the tool's scope.

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?

It explicitly states a prerequisite (captions required) and an alternative use case (multi-video synthesis → youtube_query with goal=advice), giving agents clear decision criteria.

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 discloses the return structure ('JSON including intent, result items, optional advice{}... next_steps[], and quota_cost') and mentions quota cost, which is a useful behavioral trait. It could go further by noting rate limits or error behavior, but the current disclosure is solid for a read-only query tool.

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 two sentences and front-loaded with 'Query YouTube.' The first sentence is a comprehensive list of capabilities; the second covers return format and sibling delegation. It is slightly list-heavy but each item earns its place, and it avoids unnecessary repetition of schema details.

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 complex 20-parameter tool with no output schema, the description provides a strong high-level overview of all capabilities, output fields (intent, result items, advice, next_steps, quota_cost), and sibling alternatives. It does not explain every scenario, but combined with the fully documented schema, it is sufficiently complete 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.

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

The input schema provides 100% description coverage of all 20 parameters, including enums and defaults, so the baseline is 3. The description adds minimal parameter-specific context beyond mentioning goal=advice and advice{} in the return. It does not enrich parameter meanings further, so a 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 ('Query') and enumerates distinct resource types and operations: search videos/channels/playlists, channel profiles, latest uploads, playlist items, video comments, topic research, and creator advice. It clearly distinguishes from siblings summarize_video and get_transcript by delegating single-video summaries and caption lookup to those 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?

The description explicitly names alternatives: 'Single-video summaries and verbatim caption lookup use summarize_video and get_transcript.' It also outlines the main modes via goal examples (advice, latest, research, comments, playlist, info, search) and states the return format, giving the agent enough context to choose this tool for broad YouTube queries.

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