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Recipe Generator

RecipeGenerator
Read-only

Recipe Generator: Recipe Generator creates complete cooking recipes from a dish title or meal idea. Pass any dish name to get structured ingredients with quantities, step-by-step instructions, prep time, cook time, and serving counts. (Cost: 2 credits)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>"
    },
    "name": "RecipeGenerator"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesThe name or description of the recipe you want to generate

Output Schema

NameRequiredDescriptionDefault
dataNo
errorNoError message, null on success
statusNo"ok" on success

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful non-schema context: the operation draws on an external/generative source and costs 2 credits, which matters for an agent deciding whether to call it. It stops short of noting any latency, variability, or failure modes.

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

Conciseness4/5

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

Three tight sentences that front-load the capability and end with the cost disclosure, which is well placed. Minor waste in repeating the tool name verbatim ('Recipe Generator: Recipe Generator creates...').

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

Completeness4/5

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

For a one-parameter, non-destructive generator with an output schema, the description covers purpose, input flexibility, output shape and credit cost. Nothing critical is missing, though a note on result variability or limits on dish specificity would round it out.

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

Parameters3/5

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

There is a single required parameter at 100% schema description coverage with an example, so the schema already does the work. The description's phrasing ('any dish name', 'dish title or meal idea') mildly signals that free-form descriptions are acceptable, but adds no format constraints or limits beyond that.

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 uses a specific verb+resource ('creates complete cooking recipes') and enumerates what the output contains (ingredients with quantities, instructions, prep/cook time, servings). It does not explicitly differentiate from near-neighbors like CocktailRecipe or Nutrition, but the purpose is unambiguous without opening the schema.

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

Usage Guidelines3/5

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

'Pass any dish name to get...' gives an invocation cue and implies flexibility in input ('dish title or meal idea'), but there is no when-to-use/when-not guidance and no named alternative for adjacent needs such as nutrition data or drink recipes. Usage is inferable rather than stated.

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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