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ni-c

mealie-mcp

by ni-c

Parse ingredient lines

parse_ingredients
Read-only

Parse ingredient text into structured parts (quantity, unit, food, note) with confidence scores. Preview how Mealie interprets a line before adding it to a recipe or shopping list.

Instructions

Splits free-text ingredient lines into quantity, unit, food and note, and reports how confident Mealie is about each part. Nothing is saved. Use it to check how a line will be understood before writing it to a recipe or a shopping list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parserNo"nlp" (default) uses the trained model, "brute" a rule-based split. Mealie also offers an "openai" parser; it is not exposed here because it sends every line to an external provider.
ingredientsYesIngredient lines, e.g. "2 tbsp olive oil"
Behavior5/5

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

The description reinforces the annotation's readOnlyHint=true by stating 'Nothing is saved.' It adds behavioral context: confidence reporting, parser options (nlp vs brute), and explicitly explains why the 'openai' parser is excluded due to external data sending. No contradictions; the description enriches the annotation.

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 consists of two sentences: the first states the tool's function and effect, the second provides usage guidance. Every word is meaningful, no repetition or filler. It is front-loaded with the essential purpose.

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 the tool's simplicity (2 parameters, no output schema), the description covers purpose, side effects (none), usage timing, and parser choices. It mentions confidence reporting but does not detail the output structure or how confidence is represented. While not exhaustive, it is largely complete for a parsing utility.

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 both parameters are described. The description goes beyond by explaining the 'parser' enum options in practical terms (why 'openai' is not exposed) and gives an example for 'ingredients' ('e.g. "2 tbsp olive oil"'). It also hints at the output (confidence), which is not in the schema.

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 action: 'Splits free-text ingredient lines into quantity, unit, food and note, and reports how confident Mealie is about each part.' It specifies the resource (ingredient lines) and the outcome (parsed components plus confidence). This distinguishes it from sibling tools, which are primarily about creating, updating, or retrieving recipes and shopping lists.

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 explicitly says 'Use it to check how a line will be understood before writing it to a recipe or a shopping list,' providing clear when-to-use guidance. It does not list alternative tools for when parsing is not needed (e.g., direct write tools), but the context and sibling list imply the alternatives, making the guidance useful though not exhaustive.

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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