youtube_topic_research

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 implies a read-only search operation, but it does not disclose rate limits, pagination behavior, output structure, or any side effects. The behavioral information is minimal and mostly inferred from the word 'search'.

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 a single front-loaded sentence with no filler. It communicates the main purpose efficiently, though it sacrifices explanatory depth for brevity.

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 four parameters and no annotations, the description is incomplete. The presence of an output schema helps with return values, but the description still lacks parameter semantics, usage boundaries, and behavioral caveats, so an agent cannot fully judge how to call this tool well.

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%, and the description does not explain order, format, or max_results. The only implicit mapping is the topic corresponding to 'query', but the description adds no real meaning beyond the obvious parameter names and defaults.

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 states a specific verb ('Search'), a clear resource ('YouTube videos'), and the data returned ('view counts, likes, engagement data'). This clearly distinguishes it from sibling research tools like reddit_topic_research or google_trends_explorer.

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 the tool is for YouTube topic research, so an agent could infer when to use it. However, it gives no explicit guidance on when not to use it or which alternative tool to prefer, leaving the choice to inference.

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