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Cocktail Glass

Find cocktails in a movie

find_cocktails_in_movie
Read-only

Find every cocktail that appears in a given film or TV show. Case- and diacritic-insensitive substring match against both the title and the scene description, so a character or actor works too — e.g. "Casablanca", "Bond", "Hemingway". Each result names the cocktail, the film/show title, the year, and the scene. Returns up to 60 appearances ordered oldest year first, then by cocktail name. A single cocktail can appear multiple times if it shows up in multiple scenes that match. Use this only for on-screen appearances; for a drink by name use search_cocktails, and to browse the whole catalogue use list_cocktails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
movieYesA film or TV show title, full or partial

TDQS

A5/5.0
Behavior5/5

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

The description discloses key behavioral details: case- and diacritic-insensitive matching, up to 60 results, ordering by year then cocktail name, and the possibility of a cocktail appearing multiple times. These go beyond the annotations (readOnlyHint, openWorldHint) and provide valuable context about results and edge cases.

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 well-structured and front-loaded, starting with the core purpose. Every sentence serves a purpose—usage, matching rule, output format, limits, and sibling differentiation—without redundancy. It is appropriately concise for the complexity.

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?

Given the tool's simplicity (one parameter, no output schema), the description covers all necessary aspects: what it does, how to use it, behavior details, and return format. It is self-sufficient and leaves no critical gaps for an AI agent to invoke it correctly.

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

Parameters5/5

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

The description significantly enriches the 'movie' parameter by clarifying that it is a substring matched against both title and scene description, allowing characters or actors as valid inputs (e.g., 'Bond', 'Hemingway'). This meaning is absent from the schema, which only states 'A film or TV show title, full or partial.'

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: 'Find every cocktail that appears in a given film or TV show.' It uses a specific verb and resource, and explicitly distinguishes itself from siblings by noting 'Use this only for on-screen appearances; for a drink by name use search_cocktails, and to browse the whole catalogue use list_cocktails.'

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?

Provides explicit guidance on when to use the tool: 'Use this only for on-screen appearances; for a drink by name use search_cocktails, and to browse the whole catalogue use list_cocktails.' It also explains matching behavior and gives examples, making it clear when a character or actor name can be used.

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.

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