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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: each result carries rule_number and cite_as, and the answer must cite the returned number rather than one recalled from memory, which shapes how the agent should consume output. It does not cover what happens when no rule matches.

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?

Three short sentences, front-loaded with the corpus and version, then the usage trigger, then the citation obligation. No filler and no restatement of the tool name.

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 helpfully discloses the key return fields (rule_number, cite_as) and the citation rule, which is exactly what an agent needs. For a two-parameter lookup tool this is nearly complete; the only omission is the empty-result behavior and the handoff to get_rule for follow-up detail.

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% and both parameters are documented in the schema, including the 3-12 range for top_k, so the baseline is 3. The description adds guidance on how to phrase the query (rules vocabulary, Spanish), which reinforces the schema's note but does not add syntax, format or failure-mode detail beyond it.

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 names a specific verb and resource (search the official R&A/USGA 2023 Golf Rules, Spanish edition) and even pins the edition and language, so the agent knows exactly what corpus is being queried. It does not explicitly distinguish itself from the sibling get_rule, so the agent must infer the search-vs-fetch split rather than being told.

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 gives a clear positive trigger — use it BEFORE answering any rules question — plus a concrete preparation step (reformulate the doubt into rules terminology and in Spanish). What is missing is the negative case: it never says when to call get_rule instead, e.g. when an exact rule number is already known.

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