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

With no annotations, the description carries the behavioral burden alone. It usefully discloses the data provenance (StatCounter) and freshness (latest month), which is meaningful for a data lookup, but says nothing about response shape, coverage per engine, or whether the figure is a snapshot vs. time series.

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?

A single front-loaded sentence that leads with the resource and scope. The bracketed toolbox label adds mild noise but the core information is delivered immediately with no filler.

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 zero-parameter lookup with no output schema and no annotations, the description conveys the data source, scope, recency, and example entities, which is close to what an agent needs. It stops short of describing the returned structure, but little more is reasonably required.

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 tool takes zero parameters, so there is no parameter semantics to explain and the baseline is 4. Nothing in the description contradicts or muddies the empty schema.

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?

States a specific resource (search engine market share), scope (Taiwan and global), examples (Google, Bing, Yahoo, Baidu), and data source/recency (StatCounter, latest month). An agent can identify this as a market-share lookup, though it does not explicitly distinguish itself from siblings like seo__ai_crawler_list or seo__check_site_crawlers beyond the bracketed toolbox label.

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?

No when-to-use statement, prerequisites, or named alternatives despite a large sibling set in the seo__ and llms__ families. Usage is only implied by the content description; an agent must infer the triggering scenario.

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