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

Annotations declare readOnlyHint=true, which is consistent. The description adds valuable transparency beyond that: it explains the exclusion of the current partial month, the absolute nature of the count, and that every figure links to its source. It also justifies the choice of Wikipedia over Google Trends, which is useful for trust calibration.

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 concise, front-loaded with the core purpose, and every sentence adds value: the method, the rationale, the data source comparison, and the data quirks. No filler 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?

Given the tool is a simple two-parameter read operation, the description covers the essential semantics and known limitations. It does not explain the output structure, but an output schema exists (as indicated), so that gap is acceptable. Overall, sufficiently complete for an agent to call 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 description coverage is 0%, but the description clarifies that 'entity' refers to a company and 'months' implies a time range (default 12). It does not specify exact format for entity (e.g., ticker vs name) or whether months counts trailing months, but the baseline is 3 because the description provides some compensation for the low schema coverage.

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 a specific, measurable resource ('monthly Wikipedia pageviews for its article') and frames it as a proxy for public attention, which clearly distinguishes it from financial or valuation siblings. It names alternative tools (Google Trends) but not direct siblings, yet the unique data source and purpose are unmistakable.

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?

It describes when this signal is useful ('alongside the investment case, where rising or falling attention can bear on a growth claim'), giving clear context for selection. It does not explicitly say when not to use it, but the reasoning is strong enough for an agent to infer appropriateness.

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