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

The annotations already mark the tool read-only, idempotent, and non-destructive. The description goes well beyond this by disclosing dataset composition and counts, result fields like snapshot date and source URLs, the separate unranked block for operated_by_ranked items, Rule 1 ordering behavior, and the 'evidence tier is provenance, not quality' semantics.

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 dense but every sentence earns its place: it front-loads the core purpose, then covers dataset scale, result semantics, sibling routing, and behavioral rules. It repeats no schema details and contains 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?

With no output schema, the description compensates by specifying what results carry, including source and snapshot dates, URLs, register access dates, operated_by_ranked flags, and ordering constraints. For a simple two-parameter read-only search tool, this is complete enough for correct invocation.

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 documents query well but leaves limit undocumented, giving 50% coverage. The description enriches query meaning by clarifying that categories are source/research labels rather than ranking eligibility, but it adds nothing about the limit parameter or how matching works beyond the schema's phrase 'name, domain, category or licence type'.

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 opening sentence clearly identifies a search operation over a specific dataset and lists the result types ('Nigerian-relevant businesses, institutions, websites and apps'). It also differentiates itself from siblings by stating that live rankings and place pages belong to ranked_rankings / ranked_place.

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 explicitly routes live-ranking and place-page use cases to ranked_rankings / ranked_place, providing a clear when-not-to-use signal. It does not distinguish against all siblings, but the main search-vs-live distinction is explicit and actionable.

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