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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description need not repeat safety. It adds value by explaining the score range (0.91 vs below 0.50), the interpretation (high divergence signals a content gap opportunity), and the data source, which goes beyond the 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 three concise sentences, front-loaded with the 'Use when' trigger, and every sentence adds value (purpose, return, example/source). No wasted words.

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 single-parameter, read-only tool with no output schema, the description provides robust context: use case, return type, interpretation, and source. It lacks explicit response structure or error scenarios, but these are minor given the tool's simplicity.

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 and only one required parameter, brand_name. The description implies the input is a vendor/brand via examples (Salesforce) but does not explicitly explain the parameter's meaning. It also mentions 'vendor or topic', which is slightly inconsistent with a brand_name-only input, leaving some ambiguity.

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: identifying gaps between AI platform recommendations and actual market position, and returns a platform agreement score. This specific verb ('identify gaps') and resource ('platform divergence') distinguishes it from sibling tools like get_ai_consensus_on_topic, which focuses on consensus topics.

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 opens with 'Use when identifying gaps...' providing a clear trigger for when to apply this tool. However, it does not explicitly name alternative tools or state when not to use it, though the context is clear enough.

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