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set_recipe_ingredients_parsed

Parses recipe ingredient lines and binds recognized foods and units to existing taxonomy, enabling shopping list aggregation. Lines with low confidence are stored as free text.

Instructions

Like set_recipe_ingredients, but parses each line and binds food/unit IDs.

ingredients_json is a JSON array of strings. Each is run through Mealie's NLP parser; recognized food / unit names are matched against the existing taxonomy (or created if not present), so the resulting recipe is shopping-list-aggregation-ready.

Lines whose parsed average confidence falls below min_confidence are stored as free text.

Returns a per-line summary of what was bound vs. left as free text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
ingredients_jsonYes
min_confidenceNo
Behavior4/5

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

With no annotations, the description does well by disclosing that it matches against existing taxonomy or creates new items, uses a confidence threshold, and returns a per-line summary. It could mention auth requirements or side effects like creation of foods/units.

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 concise and well-structured: opening sentence sets the context, followed by clear details on processing, matching, and output. No unnecessary words.

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

Completeness3/5

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

The description mentions return value but does not specify if ingredients are replaced or appended to the recipe. It relies on the sibling comparison for context, missing key behavioral aspects like whether slug is required and what happens to existing data.

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?

Schema coverage is 0%. The description explains ingredients_json as a JSON array of strings and min_confidence's role as a threshold. However, the slug parameter is not described, leaving a gap in understanding its purpose.

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 it is like set_recipe_ingredients but parses each line and binds food/unit IDs. It explicitly distinguishes from its sibling by highlighting the parsing and binding functionality.

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 provides context by comparing to set_recipe_ingredients, implying when to use this variant. However, it does not explicitly mention alternatives like parse_ingredient nor provide when-not-to-use scenarios.

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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