parse_recipe_ingredients

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

With no annotations provided, the description carries the full burden. It discloses the key behavioral traits: it performs a write operation (modifies the recipe), includes NLP parsing, creates new food/unit records, handles low-confidence lines by storing as free text, and returns a per-line summary. It does not cover potential rate limits or authorization requirements, but the main behaviors are transparent.

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

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

The description is well-structured, starting with a concise one-line summary followed by detailed paragraphs. It is not overly verbose, but some repetition could be eliminated (e.g., mentioning the NLP parser twice). Still, it is efficient and front-loads the key purpose.

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

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

Given the tool has only 2 parameters, no output schema, and no annotations, the description provides sufficient context for an AI agent to understand the tool's operation, including the confidence threshold and return format (per-line summary). It does not cover idempotency or undo behavior, but these are not critical for a parsing tool.

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

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

The input schema has 0% description coverage, so the description must compensate. It explains the min_confidence parameter clearly: lines below the threshold are stored as free text, with a default of 0.5. The slug parameter is implied as the recipe identifier, though not explicitly detailed. Overall, the description adds meaningful context beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool re-parses free-text ingredients and binds them to food/unit IDs. It explains the full pipeline: reading current ingredients, NLP parsing, matching, creating new records, and writing back. This distinguishes it from sibling tools like parse_ingredient (which likely only parses without binding) and set_recipe_ingredients (which may set without parsing).

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

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

The description provides context for when to use (e.g., to make ingredients eligible for shopping-list aggregation) and explains the min_confidence parameter's role. However, it does not explicitly specify when not to use it or compare with alternatives like parse_ingredient or set_recipe_ingredients_parsed. The guidelines are inferred but not directly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.