extract_recipe_ingredients

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

With no annotations provided, the description carries full burden for behavioral disclosure. It explains the success/failure condition for YouTube URLs (requires yt-dlp), caching behavior via recipe_id, and the nature of the return value. It does not elaborate on edge cases like malformed transcripts or error handling, but covers the main dependencies and output.

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 concise at 7 sentences. It front-loads the core purpose, then covers critical caveats, return value, ordering, use cases, and an example. Every sentence adds necessary information without redundancy.

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?

The tool has 3 parameters with full schema descriptions, no output schema, and moderate complexity. The description compensates by listing returned fields (ingredients with name, quantity, unit, category; plus recipe name, equipment, technique tags). It also explains caching and downstream dependencies, making the tool's role and output clear for an agent.

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?

Schema coverage is 100%, so baseline is 3. The description adds value beyond schema descriptions by explaining that transcript can be either raw text or a YouTube URL, that recipe_id is for caching and auto-derived if omitted, and by providing an example that illustrates parameter usage and return structure.

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 extracts structured recipe data (recipe name, ingredients, equipment, technique tags) from transcript text or YouTube URL. It distinguishes from sibling tools (match_ingredients_to_products and suggest_affiliate_products) by positioning itself as the first step that populates cache for downstream use.

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 explicitly says 'Call this first' and explains the dependency between this tool and its siblings. It provides clear guidance on when to use raw transcript text versus YouTube URL, including the caveat about yt-dlp installation. Use cases (recipe monetization, shoppable recipe creation) are listed.

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