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

No annotations are provided, so the description carries the full disclosure burden. It discloses the free tier, the constrained category list, and the underlying methodology (combining public market data with a geo-footprint audit). It omits any mention of auth requirements, rate limits, or how the two data sources are weighted, though the output schema mitigates some return-format ambiguity.

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?

Roughly 70 words for a read-only query tool with one parameter is appropriately sized. The primary purpose is front-loaded in the first sentence, followed by the data-source explanation and usage examples. No filler or repetition, though the trailing 'Free tier' sentence could arguably be merged with the category constraint for tighter structure.

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 simplicity (1 param, 100% schema coverage, output schema present), the description covers the core bases: what it returns, what data feeds it, when to call it, and its constraints (free tier, two available categories). Nothing critical is missing for an agent to invoke it correctly.

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 coverage is 100% — the schema already documents the category parameter as a slug with examples. The description's category list (connector, phone_case) reinforces the schema but adds no new semantic depth beyond what's already in the parameter description. Baseline 3 applies since the schema does the heavy lifting.

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 opens with a specific verb and resource ('Return the AI-recommendation leaderboard for a product category'), and explains the data combination (listed-market + tracked B2B brands). This clearly distinguishes it from siblings like audit_ai_visibility (a visibility audit) and get_checklist (a checklist), which serve different purposes.

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 concrete query examples ('who are the top Chinese suppliers of X', 'which brands does AI recommend in X') that an agent can match against user intent. It also lists the two available categories as a hard constraint. It stops short of naming explicit alternatives or stating when NOT to use it, but the usage context is well-articulated.

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