get_tag_analysis

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 describes the tool's behavior: aggregates tags, correlates with view performance, and returns top tags by frequency and average views. However, it doesn't disclose critical traits like whether this is a read-only operation (implied by 'get' in the name but not stated), potential rate limits, authentication needs, data freshness, or error conditions. The description adds some context but leaves gaps for a tool with no 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 appropriately sized and front-loaded: two sentences that directly state the tool's function and utility. The first sentence covers the core action and output, and the second sentence explains the use case. There's no wasted text, and every sentence earns its place by adding 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?

Given the tool's complexity (analyzing tags and view correlations), no annotations, and no output schema, the description is moderately complete. It explains what the tool does and its purpose, but lacks details on behavioral traits (e.g., safety, performance) and output format (beyond 'top tags by frequency and average views'). For a tool with no structured support, it should provide more context about results and limitations to be fully helpful.

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 description coverage is 100%, so the schema already fully documents the two parameters (channel_url and limit). The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain how 'limit' affects the analysis or provide examples for 'channel_url'). According to the rules, with high schema coverage, the baseline is 3 even with no param info in the description, which applies here.

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: 'Aggregates tags across a channel's videos and correlates them with view performance. Returns top tags by frequency and by average views.' It specifies the verb (aggregates, correlates, returns), resource (tags across a channel's videos), and output (top tags by frequency and average views). However, it doesn't explicitly differentiate from sibling tools like 'get_channel_topics' or 'get_video_seo_score', which might also analyze channel content.

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 implied usage guidance: 'Useful for finding which tags drive the most traffic.' This suggests the tool is for analyzing tag performance to identify traffic drivers. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., 'get_channel_topics' for topic analysis or 'get_engagement_stats' for broader metrics) or any exclusions (e.g., not for individual video analysis). The guidance is helpful but lacks specificity about sibling tool distinctions.

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