AI Collection

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

With no annotations, the description carries the burden of disclosing behavior. It mentions pagination and scoping to a category, which are useful. However, it does not explicitly state that it is a read-only operation, nor does it describe error behavior or return format. For a simple read tool, this is adequate but not rich.

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 sentences, front-loaded with the primary function and immediately followed by usage guidance. No wasted words.

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 simplicity of the tool (list with pagination) and the comprehensive parameter schema, the description covers the essential context. Since there is no output schema, a brief note on return structure would enhance completeness, but the current information suffices for a tool of this complexity.

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%, with each parameter having a description. The description itself adds no additional parameter semantics beyond what the schema already provides, so the baseline of 3 applies.

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 function: listing applications within a specific category, with pagination. It explicitly distinguishes itself from searching or getting a specific tool, making it distinct from siblings like search_tools and get_tool.

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: when exploring an area rather than searching for a specific tool. This directly contrasts with search_tools and clarifies the intended use case.

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, the description carries the burden of behavioral disclosure. It states the tool returns structured attributes, implying a read-only operation, and enumerates the attribute categories. However, it does not explicitly disclose whether it modifies data, requires auth, or handles missing tools. This is adequate but not exhaustive.

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 consists of two clear, concise sentences with no redundant wording. It front-loads the core function and then explains the returned data and purpose, earning a perfect score.

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?

For a tool with only two parameters and no output schema, the description is fairly complete: it explains the result (structured attributes) and lists the attribute categories. It does not mention error behavior or return format details, but these are not critical for a comparison 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?

Schema description coverage is 100% with both parameters (linkName_a, linkName_b) well-defined. The description adds no additional parameter context beyond the schema, so the baseline score of 3 applies.

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 uses a specific verb ('Compare') and resource ('two AI tools side-by-side'), and lists the returned attributes (pricing, deployment, modality, content policy). This clearly distinguishes it from sibling tools like get_tool (single tool) or get_alternatives (suggesting alternatives, not a direct 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 provides clear context for when to use the tool: when a client needs to recommend which of two tools fits a user's needs. It does not explicitly name alternatives or state when not to use it, but the comparison use case is evident.

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, the description carries the burden. It discloses the tool returns same-category alternatives excluding the original, which is the core behavior. The description implies read-only operation, which is accurate for a lookup tool. It does not mention any side effects or prerequisites, but given the simple read-only nature, the provided behavioral disclosure is strong.

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, front-loaded with the core purpose, followed by usage examples. Every sentence earns its place with no wasted words, achieving excellent conciseness and structure.

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 is simple (2 params, no output schema, no annotations), and the description covers the core purpose and usage. However, it omits the limit parameter from the textual description, which is a key aspect of controlling the output. The lack of any return format detail is acceptable given the simple nature, but the limit gap makes it not fully complete.

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 50%: only linkName is described, while limit has no description. The description does not compensate for the missing limit explanation—it never mentions how many alternatives are returned or that limit controls the count. Thus it adds no value for the limit parameter, which is a significant gap.

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 function: given a specific AI tool, return similar tools in the same category, excluding the original. This distinguishes it from siblings like search_tools (general search), get_tool (retrieve one), compare_tools (compare), and list_categories (list categories), showing a specific verb+resource 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 explicitly says to use it for 'what's like X?' or 'cheaper alternative to Y' questions, providing clear when-to-use guidance. However, it does not mention when not to use or name alternatives, so it falls short of the full exclusions, but the clear context earns a 4.

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 burden. It discloses return contents (name, full description, category, screenshot, additional info) and uses 'Fetch' to imply a read-only operation. It lacks edge-case details like not-found behavior, but this is acceptable for a simple read 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?

A single sentence clearly states purpose, input, and output with no filler. It is front-loaded and every clause adds value.

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?

For a one-parameter read tool with no output schema, the description is sufficient: it states the input, the action, and the returned fields. It could mention error handling, but the low complexity and strong schema coverage make the description complete enough for selection and invocation.

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 already fully documents 'linkName' with 100% coverage, including its canonical slug format. The description adds no new semantic detail beyond restating that lookup is by linkName.

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 uses a specific verb 'Fetch' and clearly identifies the resource: the full detail page for a specific AI tool by its linkName. This distinguishes it from sibling tools like search_tools and browse_category, which handle lists or categories.

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 clearly implies the use case—retrieve details for one known tool by linkName—and contrasts with broader browsing/searching tools. However, it does not explicitly name alternatives or state when not to use it.

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 must disclose behavioral traits, but it only restates the core function. It does not describe output format, ordering, how category filtering behaves, or error handling. The 'curated editorial' phrasing adds minimal behavioral context.

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 sentences, front-loaded with the action, and contains no fluff or repetition. Every phrase adds value.

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?

For a simple tool with two optional parameters and no output schema, the description conveys the core purpose and use cases. However, it lacks details about the return structure or what constitutes a 'pick', which would be useful given no output schema.

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 covers 50% of parameters; 'category' is described in the schema, and 'limit' has clear constraints (default, min, max). The description adds usage context ('top X in Y') but does not further explain parameter semantics beyond what the schema provides.

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 returns 'curated editorial picks' with an optional category scope, which is a specific verb+resource+scope. It distinguishes itself from siblings like search_tools or browse_category by emphasizing the 'editorial' nature of the results.

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?

Provides explicit usage scenarios: 'recommend the best AI tools' or 'top X in Y' questions. This clearly signals when to use the tool, though it doesn't explicitly mention alternatives or when not to use it.

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 states 'List all categories in the directory,' which implies a read-only operation and clarifies the scope (all categories, in the directory). While it does not explicitly mention side effects or permissions, the action 'list' is inherently non-mutating, and the added scope goes beyond the tool name.

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 sentences, both earning their place. The first sentence states the action and scope; the second provides usage context. It is front-loaded with the core function and contains no redundant or extraneous information.

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 (zero parameters, no output schema, no annotations), the description is complete. It fully explains what the tool does and when to use it, leaving no critical gaps. No additional return-format or prerequisite details are necessary for a simple list operation.

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 tool has zero parameters, so the schema description coverage is trivially 100%. Per the rubric, 0 parameters warrants a baseline score of 4. The description does not add parameter-specific details because there are none, which is appropriate.

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 function: 'List all categories in the directory.' The verb 'List' is specific and the resource 'categories' is well-defined. The word 'all' distinguishes it from sibling tools like browse_category or search_tools, making the purpose unambiguous.

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 clear usage context: 'Useful when the user wants to browse by topic or narrow a search by category.' This tells the agent when to invoke this tool, though it does not explicitly name alternatives or state when not to use it. This falls short of a 5 but is clear enough for a 4.

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. It discloses that the tool returns a ranked list with name, URL, and short description, and that it matches a query. This is sufficient to understand the read-only behavior and output, though it does not detail ranking semantics or edge cases.

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 sentences, front-loaded with the action and immediately states the result format and primary use case. Every sentence earns its place, with no wasted words.

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 params (1 required), no output schema, and no annotations. The description covers the core purpose, output fields, and a concrete usage scenario. While it doesn't mention category filtering or ranking order, the schema fills some gaps, and the overall description is reasonably complete for a search 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?

Schema coverage is 100%, so the baseline is 3. The description adds minor context by framing the query as 'find me a tool that does X' and mentioning output fields, but it does not add meaning to the category or limit parameters beyond what the schema already states. The description does not improve parameter understanding significantly.

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 searches the AI Collection directory and returns a ranked list with name, URL, and short description. It uses a specific verb ('Search'), resource ('AI Collection directory'), and result format, distinguishing it from sibling tools like browse_category or get_tool.

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 provides a use case: 'Use this for "find me a tool that does X" requests.' This gives solid context for when to use the tool. It does not mention when not to use alternatives, but the context is clear enough that a 4 is appropriate rather than a 5.

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