AI Collection

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

No annotations are provided, so the description bears the full burden. It clearly states the tool lists applications, is paginated, and restricts to a category. However, it omits details like result ordering or any side effects, which would elevate it to 5.

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, front-loaded sentence that efficiently conveys purpose and usage guidance without unnecessary 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 tool's simplicity (no output schema, low parameter count), the description is complete enough. It explains the action, scope, and pagination. A 5 would require some mention of the return format or default behaviors.

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 all three parameters already well-described in the schema. The description does not add additional semantic context beyond the schema, so baseline 3 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 uses a specific verb ('List') and resource ('applications inside a specific category') with pagination context. It clearly distinguishes the tool's exploratory purpose from sibling tools like 'search_tools'.

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?

Explicitly states when to use this tool: 'Use this when the user wants to explore an area rather than search for a specific tool.' This provides clear guidance and implicitly contrasts with search_tools.

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 full burden. It does not disclose whether the tool is read-only, has side effects, requires authentication, or has rate limits. The description only mentions returning attributes, but does not state safety or behavioral traits beyond that.

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 every word adds value. No redundancy or unnecessary details.

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 (2 parameters, no output schema, no nested objects), the description provides enough context: what it does, what it returns, and the purpose. However, it lacks behavioral transparency (e.g., read-only hint) which would improve completeness.

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 clear descriptions for both linkName_a and linkName_b. The description adds that these are 'canonical linkName' and that they are used to compare the two tools, but does not add significant meaning beyond the schema. Baseline 3 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 verb 'compare' and the resource 'two AI tools', and specifies the output: structured attributes like pricing, deployment, modality, content policy. This distinguishes it from siblings like get_tool (single tool) or search_tools (search).

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 a use case (side-by-side comparison for recommendation) but does not explicitly state when to use this tool versus alternatives (e.g., when to use compare_tools vs get_alternatives). No exclusion criteria or prerequisites are mentioned.

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 provided, so description carries full burden. States behavior: returns similar tools in same category, excluding original. However, does not disclose how similarity is determined, ordering, or behavior on no results.

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?

Two sentences, each serving a purpose: first states function, second provides usage examples. 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?

Description covers core purpose and usage context. Lacks information about output format (e.g., list of tool names) but sufficient for intended queries. With no output schema, could be more 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 described). Description adds meaning to linkName by linking to 'specific AI tool' and 'cheaper alternative'. limit parameter is not elaborated beyond schema defaults.

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?

Description clearly states the verb 'return' and resource 'similar tools' with context 'same category, excluding the original'. It also provides example queries, distinguishing it from sibling tools like get_tool or search_tools.

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?

Explicit usage examples ('what's like X?' or 'cheaper alternative to Y') give clear context. Does not mention when not to use, but examples imply appropriate scenarios.

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 lists what is returned (name, description, category, screenshot, additional info), indicating a read operation. It does not mention error handling or prerequisites, but the behavior is clear for a fetch.

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?

Two concise sentences, front-loaded with the action. Every sentence adds value with no repetition or fluff.

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 output schema, the description lists return fields. It covers the essential aspects for a fetch tool, though it could mention the structure of the screenshot or error cases. Overall sufficient.

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 a descriptive parameter description. The tool description only says 'by its linkName', which adds no new meaning beyond the schema's 'canonical linkName (slug)'. Baseline 3 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 explicitly states 'Fetch the full detail page for a specific AI tool by its linkName', using a specific verb and resource. It clearly distinguishes from siblings like search_tools (list) and get_alternatives (alternatives).

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?

Implicitly, it tells the agent to use this when they have a linkName and need full details, but no explicit when-not-to-use or alternatives compared to siblings like browse_category or search_tools.

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 provided, so description carries full burden. It discloses that picks are curated and can be scoped by category, but lacks details on safety (read-only), data freshness, or return format. Adequate 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?

Two sentences, extremely concise. First sentence defines functionality and conditional behavior, second sentence gives usage examples. No fluff.

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 covers main functionality and when to use. Lacks mention of return format or limit parameter details, but overall sufficient.

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% (category has description, limit does not). Description adds meaning by linking limit to the 'top X' example, but does not explicitly describe limit's default or range. Adds some value beyond schema, but not fully compensating.

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?

Description clearly states it returns curated editorial picks, optionally filtered by category, and provides example use cases like 'recommend the best AI tools' or 'top X in Y' questions. This distinguishes it from siblings like browse_category or search_tools.

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?

Explicitly specifies when to use: for 'recommend the best AI tools' or 'top X in Y' questions. Does not explicitly mention when not to use or list alternatives, but the context and sibling names imply differentiation.

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 provided, so description carries full burden. Describes a simple read operation, but lacks details on potential limitations like number of categories, pagination, or authentication needs. However, given the simplicity, a score of 3 is adequate.

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?

Two effective sentences that front-load the action and add value. No unnecessary 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?

Tool has no parameters and no output schema. Description fully covers purpose and usage context. Complete for this simple 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?

No parameters in schema, so schema coverage is 100%. Description does not need to add parameter info. Baseline 4 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?

Clearly states 'List all categories in the directory' with a specific verb and resource. The additional sentence about browsing by topic or narrowing searches distinguishes it from sibling tools like search_tools or browse_category.

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 usage context: 'Useful when the user wants to browse by topic or narrow a search by category.' While it doesn't explicitly say when not to use it or list alternatives, the guidance is clear for typical scenarios.

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 full burden. It implies read-only behavior ('Search'), which is appropriate for a search tool. It does not detail side effects or authorization needs, but the action is inherently non-destructive and well-understood.

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 every sentence serves a purpose. No redundant or filler content.

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 (3 parameters, no output schema, no annotations), the description adequately covers purpose, return format, and usage example. It could mention pagination or ranking details, but it is complete enough for effective use.

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 three parameters with descriptions (query, limit, category). The description adds no additional meaning beyond the schema beyond stating the overall purpose of the query.

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 ('Search') and resource ('AI Collection directory'), and clearly states the return format ('ranked list with name, URL, and short description'). It distinguishes from siblings like 'get_tool' by providing a usage example ('find me a tool that does X').

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 'Use this for 'find me a tool that does X' requests,' giving clear context for when to use this tool. It does not explicitly mention when not to use it or list alternatives among siblings, but the usage example effectively implies differentiation.

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