Get personalized recommendations

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

The AI is a read-only, idempotent operation, which the annotations already disclose. The description adds a light behavioral fact: it turns the user's stated preferences into a set of coffee picks. It doesn't describe the output format, pagination behavior, or how the bouncer factors in each parameter, but given the annotation context, this 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?

It is a single, short sentence that is front-loaded with the main result: 'Personalized coffee picks.' The sentence packs all of the key inputs into a natural summary with no filler, making it ideal for scanning by an agent.

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?

The tool is a read-only, logically simple recommendation engine; the schema covers parameter types and defaults (limit default 5, allowed enums). The description adequately describes what the tool returns (coffee picks). An output schema would be helpful, but the absence is not critical here because the tool's purpose and inputs are fully identified. A small gap: it doesn't explicitly note that 'limit' controls the maximum number of picks, but that is already in the schema.

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 80% (4 of 5 parameters have descriptions), so the schema already documents most fields. The description adds small real-world semantics: 'budget in USD' and 'the brew gear you own' as a way to describe brew_method. It does not significantly clarify the roast_level or limit, but those are already covered by the schema or acceptable defaults.

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 opens with 'Personalized coffee picks', which clearly states that this tool returns a set of recommendations based on the user's taste profile. It names the four key inputs (flavor preferences, budget, roast, brew setup) and effectively sets it apart from the sibling search and similarity tools. It doesn't explicitly single out any sibling, but the core purpose is unambiguous.

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 the expected usage: provide preferences, budget, roast, and brew gear to receive tailored coffee recommendations. But it doesn't tell the agent when to prefer this tool over alternatives like find_similar_coffee, what_to_brew, or difficult search_coffees. There is no exclusion or explicit contrasting with the sibling tools.

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