Brew guidance for a specific coffee: curated recipes from the Percolate catalog (ratio, temperature, grind) when available, or a roast-based starting point. Optionally scoped to your brew method.
| Name | Required | Description | Default |
|---|---|---|---|
| coffee | Yes | Coffee id or name | |
| brew_method | No | Your brewer, e.g. 'V60', 'espresso', 'french press' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral detail: curated recipes are offered when available, otherwise a roast-based fallback is used. No contradiction with annotations.
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 a single well-structured sentence that front-loads the core purpose and includes only relevant details about fallback behavior and optional scoping. No wasted words.
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 tool with annotations covering safety and a schema covering all parameters, the description conveys the core behavior, fallback logic, and optional filtering. There is no output schema, but the description reasonably communicates what kind of guidance will be returned.
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 100%, so the parameters are already well documented. The description adds minimal extra semantic value beyond mentioning the optional brew-method scoping, which is already captured in the schema.
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 provides brew guidance for a specific coffee, with curated recipes or a roast-based starting point. It is distinguishable from search/recommendation tools in spirit, but it does not explicitly differentiate itself from siblings like what_to_brew or get_recommendations.
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 establishes a clear usage context: use when you have a specific coffee and want brew guidance, optionally filtered by brew method. It does not explicitly state when not to use it or name alternatives, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct job: searching, fetching details, comparing, finding similar coffees, personalized recommendations, trending picks, immediate what-to-brew suggestions, and brew dial-in guidance. No two tools appear to perform the same core action.
Most tools follow a readable snake_case verb_noun pattern (compare_coffees, get_coffee, search_coffees), and the exceptions (trending_coffees, what_to_brew, find_similar) are still intuitive and consistent in style. The deviations are minor rather than chaotic.
8 tools is a well-scoped size for a specialty-coffee discovery and recommendation server. Each tool covers a different user-facing step without bloating the surface or leaving the domain feelig thin.
The tool surface covers the main coffee journey: search, inspect, compare, find similar, get personalized/or-trending/now suggestions, and brewing guidance. Minor gaps exist around enumerating available option values or filtering directly by flavor tags, but agents can work around these using search and the structured data returned.