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counterbeing

mealie-mcp-ts

by counterbeing

parse_recipe_ingredients

Convert free-text recipe ingredients into structured quantity, unit, and food fields by parsing each ingredient, creating missing records, and saving updates to the recipe.

Instructions

Re-parse all ingredients for an existing recipe so each has proper quantity, unit, and food fields instead of free-text notes. Fetches the recipe, sends each ingredient note through the Mealie ingredient parser, creates any missing unit/food records, and saves the structured ingredients back to the recipe. Use this after importing a recipe from a URL whose ingredients came in as plain text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesRecipe slug whose ingredients should be parsed and structured
Behavior4/5

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

The description thoroughly discloses the multi-step process: fetches the recipe, parses each ingredient, creates missing unit/food records, and saves structured ingredients back. This reveals the side effect of creating records, which is crucial since no annotations are provided.

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?

Three sentences, each earning its place: the first states purpose and outcome, the second details the process and side effects, and the third gives usage guidance. There is no filler or redundancy.

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 single-parameter mutation tool with no output schema, the description covers the trigger, step-by-step behavior, side effects, and use case. It does not mention return values, but that is not critical for such an operation, leaving only a minor gap.

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 covers slug with a clear description ('Recipe slug whose ingredients should be parsed and structured') at 100% coverage. The description does not add extra parameter semantics beyond confirming the fetch-save behavior, so baseline 3 is fair.

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 uses a specific verb 'Re-parse' and clearly identifies the resource: 'ingredients for an existing recipe.' It also states the transformation goal ('proper quantity, unit, and food fields instead of free-text notes'), which distinguishes it from import or update 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?

Explicitly instructs to use it 'after importing a recipe from a URL whose ingredients came in as plain text,' providing a clear when-to-use context. It implies the tool is for existing recipes but does not explicitly list alternatives or when-not-to-use, so a 4 is appropriate.

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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