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Get a raw recipe

get_recipe

Get one Heathy recipe in full: ingredients with their food slugs and gram weights, method, per-serving nutrition, allergens and carbon footprint. All recipes are original, strictly raw / no-cook and vegan. The nutrition is computed from the ingredient food rows rather than entered by hand, so each figure traces back to the national datasets lookup_food cites. Accepts a slug or the recipe name; use search_recipes to find one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recipeYesRecipe slug or name — e.g. "dragon-fruit-coconut-breakfast-bowl".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It adds meaningful context: recipes are original, strictly raw/no-cook, and vegan, and nutrition figures are computed from ingredient food rows rather than entered by hand. It stops short of describing response structure or error behavior, but the provenance and content constraints are genuinely useful.

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?

The description is four sentences and wastes little space: it front-loads the purpose, then adds constraints, provenance, and usage guidance. Each sentence earns its place, though the typo 'Heathy' is a minor blemish.

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?

Given that there is no output schema, the description compensates by enumerating the returned recipe components and clarifying the accepted input format plus the search_recipes alternative. It could mention invalid-slug or error behavior, but it is sufficient for an agent to select and invoke the tool correctly.

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?

The input schema already fully describes the single parameter as 'Recipe slug or name' with an example, giving 100% schema coverage. The description's phrase 'Accepts a slug or the recipe name' adds no new semantic information beyond what the schema provides, so the baseline of 3 is appropriate.

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 opens with a specific action and target — 'Get one Heathy recipe in full' — and enumerates exactly what that includes: ingredients with slugs and gram weights, method, per-serving nutrition, allergens, and carbon footprint. This clearly distinguishes it from sibling tools like search_recipes, which finds recipes rather than retrieving a full one.

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?

The description states that the tool accepts a slug or recipe name and explicitly directs users to search_recipes when they need to find one. This is a clear routing instruction for the main alternative, though it does not enumerate exclusions for the other sibling tools.

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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