get_comment_keywords

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 effectively describes key traits: the deterministic word frequency analysis method (no LLM or sentiment model), the scope (extracts from comments), and the purpose (understanding audience resonance). It doesn't cover potential limitations like rate limits, authentication needs, or error conditions, but provides sufficient operational context for basic use.

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 in two sentences: the first states the core functionality and method, the second provides usage context. Every phrase adds value without redundancy, and it's appropriately front-loaded with the primary purpose. No wasted words or unnecessary elaboration.

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 (frequency analysis), no annotations, and no output schema, the description provides adequate but incomplete context. It explains what the tool does and its method, but doesn't describe the return format (e.g., list of keywords with counts), how 'meaningful words' are determined, or what happens with insufficient comments. For a tool with no output schema, more detail about results would be beneficial.

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 documentation for all three parameters (video_id, limit, top_n). The description adds minimal parameter semantics beyond the schema, only implying that 'most frequent meaningful words' relates to the top_n parameter. Since the schema does the heavy lifting, the 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 clearly states the tool's purpose with specific verbs ('extracts', 'analysis') and resources ('most frequent meaningful words from a video's comments'), distinguishing it from siblings like get_video_comments (which fetches comments) or get_channel_topics (which analyzes channel-level topics). It explicitly mentions the deterministic word frequency approach, setting it apart from potential LLM-based alternatives.

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 understanding what topics and themes resonate with your audience'), which implicitly differentiates it from siblings focused on metrics (get_engagement_stats), SEO (get_video_seo_score), or raw data (get_video_comments). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the siblings.

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