get_ingredient_info

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by specifying the comprehensive nature of the response format (detailed information, complete recipes, substitution notes, etc.), which goes beyond basic parameter documentation. However, it doesn't address potential limitations like rate limits, authentication needs, or what happens with invalid ingredient names, leaving some behavioral aspects unclear.

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 with clear sections (Use Cases, Response Format, Examples) that make it easy to scan. Every sentence adds value without redundancy. The examples provide concrete illustrations of tool usage. The formatting with bold headers enhances readability while maintaining brevity.

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 an output schema (context signals indicate 'Has output schema: true'), the description doesn't need to explain return values in detail. The 'Response Format' section provides a helpful overview of what information will be returned, which complements the output schema well. The examples further clarify expected behavior. For a single-parameter lookup tool with good output schema support, this description provides adequate context.

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 100% description coverage, with the single parameter 'ingredient_name' clearly documented in the schema. The description doesn't add any additional parameter semantics beyond what the schema provides (e.g., format examples, validation rules, or edge cases). According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

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's purpose as 'Get comprehensive information about cocktail ingredients and their usage,' which is a specific verb+resource combination. It distinguishes from sibling tools like 'get_recipe' (which focuses on specific cocktail recipes) and 'smart_search_cocktails' (which searches for cocktails) by emphasizing ingredient-centric information. However, it doesn't explicitly contrast with these siblings in the description text itself.

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 'Use Cases' section provides clear context for when to use this tool, including ingredient research, substitution guidance, usage exploration, and flavor profile understanding. This gives practical guidance on appropriate scenarios. However, it doesn't explicitly state when NOT to use this tool or mention alternatives like the sibling tools for recipe-specific or cocktail-search tasks.

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