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 clearly states that results are never averaged, explains the semantics of `engines` and `providers`, and emphasizes that `not_measured` is not a zero. It also warns that AI answers are non-deterministic and advises reading trends rather than single measurements. This is substantial contextual detail beyond what any schema or annotation would provide.

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 moderately long but each sentence adds value: it states the purpose, explains the reporting units, defines field meanings, and gives interpretation guidance. It is front-loaded with the primary purpose and then organized logically. While it could be tightened slightly, it is well-structured and not padded.

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 there is no output schema, the description adequately describes the return structure (engines and providers) and clarifies important states. It provides enough detail for an agent to understand what the tool returns and how to interpret it. It does not mention pagination or error cases, but for a simple data retrieval tool with only two parameters, the description is largely complete.

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 already has 100% description coverage for both parameters (appId and country). The tool description does not add any parameter-specific explanation beyond what the schema provides, so it does not enhance the semantics. The baseline of 3 is appropriate because the schema already carries the documentation load.

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 states precisely what the tool does: determines whether AI assistants name the app when asked about its category, reported per engine. It names the engines (Claude, ChatGPT, Gemini) and clarifies that it is an observation, not a ranking. This clearly distinguishes it from sibling tools like get_charts or get_reviews, which address different data domains.

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 implicit usage context (e.g., it measures AI visibility, so use when you need that) and important interpretive guidance (not a ranking, read trends). However, it does not explicitly mention when not to use it or point to an alternative tool. Since no sibling overlaps directly, the lack of explicit exclusion is minor, but there is no direct 'when to use this' statement.

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