cocktails

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'finds' cocktails, implying a read-only operation, but does not disclose any behavioral traits such as rate limits, pagination, error handling, or response format. The description is minimal and lacks critical operational details for effective use.

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 a single, efficient sentence that front-loads the core functionality ('Find cocktails that use a specific ingredient') and includes helpful examples. There is no wasted text, and it is appropriately sized for a simple tool with one parameter.

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 lack of annotations and output schema, the description is incomplete. It does not address behavioral aspects like response format, error conditions, or usage constraints. For a tool with no structured metadata, the description should provide more context to ensure the agent can use it correctly, but it falls short.

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%, with the parameter 'ingredient' well-documented in the schema. The description adds minimal value beyond the schema by providing examples ('e.g., "vodka", "lime juice", "gin"'), but does not explain semantics like case sensitivity, partial matches, or validation rules. Baseline score of 3 is appropriate as the 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 the tool's purpose with a specific verb ('Find') and resource ('cocktails'), specifying it filters by ingredient. It distinguishes from 'get_cocktail' (likely retrieves a single cocktail), 'random_cocktail' (random selection), and 'search_cocktails' (broader search), but does not explicitly contrast with siblings. The examples ('vodka', 'lime juice', 'gin') help clarify the scope.

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 implies usage when looking for cocktails based on a specific ingredient, but does not provide explicit guidance on when to use this tool versus alternatives like 'search_cocktails'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool name and description alone.

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?

No annotations are provided, so the description carries the full burden. It states it retrieves details but does not disclose behavioral traits such as error handling (e.g., what happens if the ID is invalid), rate limits, or authentication needs. This leaves gaps for a tool with no annotation coverage.

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 a single, efficient sentence that front-loads the purpose ('Get full details') and includes essential context ('by its TheCocktailDB ID, including all ingredients and instructions') with zero waste, making it highly concise and well-structured.

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 low complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose but lacks details on behavioral aspects like error handling or output format, which are important for a read operation with no structured output guidance.

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 schema description coverage is 100%, so the schema already documents the 'id' parameter with examples. The description adds minimal value by mentioning 'TheCocktailDB ID' but does not provide additional syntax or format details beyond what the schema provides, meeting the baseline for high 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 specific action ('Get full details'), the resource ('a cocktail'), and the method ('by its TheCocktailDB ID'), distinguishing it from sibling tools like 'random_cocktail' or 'search_cocktails' which serve different purposes.

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 implies usage when you have a specific cocktail ID and need comprehensive details, but it does not explicitly state when not to use it or name alternatives like 'search_cocktails' for when you don't have an ID. The context is clear but lacks explicit exclusions.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'full details including ingredients and instructions,' which adds useful context about the output format. However, it lacks information on potential behavioral traits such as rate limits, error conditions, or whether the randomness is truly uniform, leaving gaps in transparency.

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 a single, well-structured sentence that efficiently conveys the tool's purpose and key details ('full details including ingredients and instructions'). It is front-loaded with the main action and contains no redundant information, making it highly concise and effective.

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 simplicity (0 parameters, no annotations, no output schema), the description is adequate but minimal. It explains what the tool does but does not address potential complexities like output format details or error handling. For a read-only tool with no parameters, this is acceptable but leaves room for more 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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose. This meets the baseline for tools with no parameters, as it avoids unnecessary details.

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 action ('Get a random cocktail') and the resource ('cocktail'), specifying that it returns 'full details including ingredients and instructions.' However, it does not explicitly differentiate from siblings like 'get_cocktail' (which likely fetches a specific cocktail) or 'search_cocktails' (which allows filtering), leaving some ambiguity in comparison.

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 implies usage when a random cocktail is needed, but it does not provide explicit guidance on when to use this tool versus alternatives like 'cocktails_by_ingredient' or 'get_cocktail.' No exclusions or prerequisites are mentioned, leaving the agent to infer context from tool names alone.

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 mentions the tool returns a list with key details, but lacks information on error handling, rate limits, authentication needs, or pagination. For a search tool, this is a significant gap in transparency, though it doesn't contradict any annotations.

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 two concise sentences that efficiently convey the tool's function and output without unnecessary words. It is front-loaded with the core purpose, making it easy for an agent to parse quickly.

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 no annotations and no output schema, the description is incomplete for a search tool. It lacks details on the structure of returned data (e.g., what 'key details' include), error cases, or performance constraints, which are crucial for effective tool invocation by an AI 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?

The input schema has 100% description coverage, with the 'query' parameter documented as 'Cocktail name or partial name to search for'. The description adds no additional parameter details beyond implying the search is name-based, so it meets the baseline of 3 where the 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 the tool's purpose: 'Search for cocktails by name' specifies the verb (search) and resource (cocktails), and 'Returns a list of matching cocktails with key details' indicates the outcome. However, it doesn't explicitly differentiate from sibling tools like 'cocktails_by_ingredient' or 'get_cocktail', which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives like 'cocktails_by_ingredient' (search by ingredient) or 'get_cocktail' (retrieve a specific cocktail). It only states what the tool does, without context for selection among siblings, leaving the agent to infer usage.

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