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

Annotations already establish that this is read-only, idempotent, and non-destructive. The description adds useful behavioral detail beyond that: partial case-insensitive matching and the specific fields returned for each match. It does not go into pagination behavior, but the schema covers page and per_page details, so the additional context is sufficient.

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 well-structured sentence conveys the search behavior, matching semantics, and return fields. There is no redundancy or 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 low-complexity search tool, the description, schema, and annotations together provide enough information to call it correctly. It could be slightly stronger by naming sibling alternatives, but nothing critical is missing 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?

Input schema coverage is 100%, and the schema already documents query, page, and per_page with defaults and constraints. The description restates name matching but adds no meaningful parameter detail beyond the schema, so the baseline score of 3 is appropriate.

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 verb and resource: searching breweries by name, and clarifies the matching behavior as partial and case-insensitive. It does not explicitly call out sibling tools like openbrewery.breweries.list or openbrewery.breweries.random, but the by-name search intent is clear enough to distinguish it from them.

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 implies this tool is for finding breweries by name, which gives some usage context. However, it does not explicitly mention when to use list or random instead, and provides no exclusions or alternative routing.

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