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.

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 effectively describes performance characteristics (e.g., '5-10x faster than sequential requests', '70%+ cache hit rate'), error handling ('individual failures don't break entire batch'), and response format details. However, it doesn't explicitly mention whether this is a read-only operation or if it requires authentication, leaving some behavioral aspects implicit.

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 (e.g., 'Batch Processing System', 'LLM Usage Patterns'), but it's quite lengthy with multiple subsections and performance examples. While all content is relevant, it could be more front-loaded; the core purpose appears in the first sentence, but detailed usage patterns come later. Some redundancy exists between the description and schema descriptions.

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's complexity (6 parameters, batch processing capabilities) and the presence of an output schema (implied by 'Has output schema: true'), the description is highly complete. It covers purpose, usage guidelines, behavioral traits, parameter interactions, performance characteristics, and response format details. The output schema likely handles return values, so the description appropriately focuses on other contextual aspects.

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 description coverage is 100%, so the schema already documents all six parameters thoroughly. The description adds value by organizing parameters into usage patterns ('Input Methods') and explaining how they interact (e.g., 'Mixed Batch' combining names and IDs), but doesn't provide significant semantic information beyond what's in the schema descriptions. This meets the baseline for high schema coverage.

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: 'Advanced recipe retrieval with powerful batch processing for multiple cocktails.' It specifies the verb ('retrieval') and resource ('recipes'), and distinguishes from siblings like 'get_ingredient_info' and 'smart_search_cocktails' by focusing on recipe retrieval rather than ingredient information or search functionality.

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 explicit guidance on when to use this tool through the 'LLM Usage Patterns' section, listing scenarios like single recipe requests, recipe comparison, menu planning, variation exploration, and research mode. It also offers detailed 'Input Methods' with specific use cases for each parameter combination, helping the agent choose appropriate inputs based on context.

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

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 does an excellent job describing performance characteristics (parallel processing, 5-10x speed improvement, caching system, error resilience), operational limits (default: 20, max: 50), and response format details. However, it doesn't mention authentication requirements, rate limits, or potential costs, which would be helpful for a production tool.

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 (Batch Processing System, Use Cases, Examples, Response Format, Performance Features) and uses emojis for visual organization. While comprehensive, it's somewhat lengthy; some details about caching TTL management and error isolation could be condensed. Every section adds value, but the front-loaded 'PREFERRED TOOL' designation could be more prominent.

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's complexity (13 parameters, batch processing capabilities) and the presence of an output schema (which handles return value documentation), the description provides excellent contextual completeness. It covers purpose, usage scenarios, behavioral characteristics, performance features, and response format details, making it fully adequate for an agent to understand when and how to use this tool effectively.

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 description coverage is 100%, so the schema already documents all 13 parameters thoroughly. The description adds some context about batch processing implications (e.g., 'Triggers similarity batch processing' for similar_to, 'Combines with other filters for batch processing' for ingredient) but doesn't provide significant additional parameter semantics beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 this is an 'Advanced cocktail search with intelligent batch processing and complete recipes,' specifying the verb (search), resource (cocktails), and key capabilities (batch processing, complete recipes). It distinguishes from sibling tools get_ingredient_info and get_recipe by emphasizing search functionality with batch processing rather than single-item retrieval.

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 explicit usage guidance with a dedicated 'Use Cases' section listing six specific scenarios (general searches, similarity queries, ingredient-based, flavor profiles, complex filtering, batch comparisons). It also includes batch processing examples showing how to structure different types of queries, giving clear when-to-use examples.

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