Cocktail Glass

Find makeable cocktails

find_makeable_cocktails

Read-only

Given the ingredients you have on hand, find every cocktail you can make completely — one where you already have all of its ingredients. Garnishes are treated as optional and plain water is assumed available; soda and tonic water are not. Matching is word-based, not substring: "gin" matches "London dry gin" but not "ginger beer", and generic terms do not match product-class extras ("gin" will not cover "sloe gin" or "orange bitters"). Returns two lists: "makeable" (drinks you can make now, up to 60) and "almostMakeable" (drinks exactly one ingredient short, up to 25, each naming the missing ingredient). Drinks needing two or more extra ingredients are omitted entirely. Both lists are ordered simplest first — fewest distinct ingredients in the full recipe, then alphabetical by name. Use this for multi-ingredient "what can I make?" questions; for a single ingredient use find_cocktails_by_ingredient.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ingredients`	Yes	The ingredients you have available — spirits, liqueurs, juices, mixers, etc.

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the read-only and closed-world annotations, the description reveals key behavioral details: matching is word-based not substring, garnishes are optional, water is assumed but soda/tonic are not, list caps (60/25), ordering by simplicity then name, and omission of drinks needing 2+ missing ingredients. This is exceptionally transparent and adds substantial value over the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence carries meaningful information: purpose, matching rules, list contents, limits, ordering, and usage guidance. It is front-loaded with the core purpose and structured logically, with no wasted words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having only one simple parameter and no output schema, the description fully explains return values (makeable and almostMakeable lists, missing ingredient names, caps, ordering) and edge cases. It is self-contained and leaves no significant gaps for an agent to guess about.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully describes the 'ingredients' parameter with a clear description and examples, so the baseline is 3. The tool description adds nuance about acceptable ingredient terms and matching behavior, plus optional garnishes, which helps the agent construct valid inputs. This extra context justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: given ingredients, find fully makeable cocktails and near-makeable ones. It uses a specific verb ('find') and resource ('makeable cocktails'), and explicitly distinguishes from the sibling tool for single-ingredient searches, making its purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the agent when to use this tool: 'Use this for multi-ingredient "what can I make?" questions; for a single ingredient use find_cocktails_by_ingredient.' This provides clear usage context and names the alternative, exactly what good usage guidelines require.

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

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TDQS

A4.9/5.0

Disambiguation5/5

Each tool has a distinct and non-overlapping purpose: single ingredient search, multi-ingredient makeable search, movie search, recipe retrieval, random suggestion, and name search. No ambiguity between tools.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (find_cocktails_by_ingredient, find_cocktails_in_movie, find_makeable_cocktails, get_cocktail_recipe, random_cocktail, search_cocktails). The verbs clearly indicate the action and the nouns the subject.

Tool Count5/5

With 6 tools, the server is well-scoped for a cocktail discovery and recipe service. It covers all essential interactions without excessive or insufficient tools.

Completeness4/5

The tool surface covers the main workflows: ingredient-based discovery, movie-based discovery, name search, recipe retrieval, and random suggestion. A minor gap is the lack of a tool to list all cocktails without filters, but overall it's complete for a read-only catalogue.

Resources

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