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Let Gusto decide what's for dinner

whats_for_dinner

What should I cook tonight with what I have? Gusto, What's Cooking's chef, decides one complete dinner and returns the full recipe, steps, and a list of what to buy. Inputs (all optional): have (what is already in the kitchen, a list or a sentence), cuisine, servings (default 2, max 24), diet (hard constraints such as vegetarian, vegan, gluten-free), max_minutes, mood, skill. Salt, pepper, oil and water are assumed on hand. Call again with different inputs for another idea. Example: {"have":["chicken"],"cuisine":"Mexican"}

Input Schema

NameRequiredDescriptionDefault
dietNoHard dietary constraints, e.g. ["vegetarian"].
haveNoIngredients already on hand, e.g. ["chicken","rice"] or "chicken, rice and an onion".
moodNoe.g. cozy, light, celebratory.
skillNoe.g. beginner, confident.
cuisineNoe.g. Mexican, Thai, Italian.
servingsNoHow many people. Default 2.
max_minutesNoTotal time limit in minutes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

It discloses the return contents, optional inputs, defaults, and kitchen assumptions such as salt/pepper/oil/water being on hand. The annotations already cover safety hints, and the description adds useful behavioral context, though it does not explain any state persistence despite readOnlyHint=false.

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 front-loaded with purpose and output, then compactly covers inputs, assumptions, repeat behavior, and an example. It is dense but every sentence adds useful information.

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 seven optional parameters, no output schema, and annotations that cover safety, the description is complete enough: it explains what is returned and how the main inputs behave. An agent has sufficient information to invoke it correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds useful semantics: all inputs are optional, servings default/max, diet as hard constraints, and assumed pantry staples. It also provides an example invocation.

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

Purpose4/5

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

The description states a specific verb and resource: Gusto decides one complete dinner and returns the recipe, steps, and shopping list. It is clear what the tool does, but it does not explicitly distinguish itself from the sibling browse/recipe tools.

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

Usage Guidelines4/5

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

It gives a clear usage context ("What should I cook tonight with what I have?") and says to call again with different inputs for another idea. It does not explicitly state when not to use it or name the alternative siblings.

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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