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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds the specific metric (unprompted AI mention frequency), a concrete example, and the data source (Stratalize AI citation index), providing useful behavioral context beyond the annotations. No contradictions or hidden behaviors are evident.

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 four short sentences: usage, return type, example, and source. Information is front-loaded with the use case, followed by concise details. No wasted words or 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?

For a single-parameter read-only tool with no output schema, the description covers what it does, when to use it, and provides an illustrative example. It omits details about result limits or edge cases, but the simplicity of the tool makes this acceptable.

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 only one parameter 'category' with no description (0% coverage). The description compensates by stating it applies to a software category and providing the CRM example, making the parameter's meaning clear. It doesn't define exact value formats, but the example implies common category names.

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 returns vendors ranked by unprompted AI mention frequency in a software category. The example with CRM and specific mention counts makes the purpose concrete. It distinguishes from siblings by focusing on AI-generated recommendation visibility, not generic rankings or other AI metrics.

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?

Explicitly starts with 'Use when assessing brand visibility in AI-generated recommendations or researching which vendors dominate AI platform responses in a software category,' providing clear context for when to invoke this tool. It does not mention alternatives or exclusion criteria, but the intended use case is unambiguous.

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