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

Annotations already establish read-only and idempotent behavior; the description adds valuable context by noting deterministic scoring over Percolate's structured tasting data. This tells the agent that results are reproducible and grounded in structured attributes rather than subjective or opaque ranking. It does not cover edge cases like no matches or invalid coffee references, but for a simple read-only tool this is acceptable.

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 two tight sentences with no wasted words. The first sentence front-loads the purpose and ranking criteria, while the second adds a meaningful note about determinism and the data source. Every element earns its place.

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 read-only tool, the description, schema, and annotations together cover input format, ranking behavior, result count limits, and safety profile. The only minor gap is information about behavior when no similar coffees are found, but that is not critical for a listing tool. Overall this is complete enough for an agent to invoke 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?

Both parameters are fully documented in the schema: 'coffee' accepts an id or name, and 'limit' specifies max results with a default of 5. The description does not add further parameter-level meaning beyond restating the ranking logic, so it appropriately relies on the schema's 100% coverage. No gaps require compensation.

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 clearly states the tool's function: return coffees similar to a given one, ranked by shared flavor notes and roast/body/acidity/sweetness proximity. This specifies a concrete verb, resource, and output ordering, which is more specific than a vague 'find similar'. However, it does not explicitly contrast with sibling tools like get_recommendations or search_coffees, so some differentiation is left to inference.

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 intended use case is implied: provide a reference coffee and obtain ranked similar coffees based on tasting data. But there is no explicit guidance on when to choose this tool over alternatives, such as search_coffees for text queries or get_recommendations for curated suggestions. The description implies usage but does not state exclusions or alternatives.

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