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 mentions the tool returns topic IDs and is a 'first step,' but lacks details on permissions, rate limits, error handling, or whether it's read-only (implied by 'List' but not explicit). For a tool with 4 parameters and no annotation coverage, this is a significant gap in 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 front-loaded with the core purpose in the first sentence, followed by usage guidance and output details in two additional sentences. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 complexity (4 parameters with 0% schema coverage, no annotations, no output schema), the description is incomplete. It explains the purpose and usage well but omits critical details about parameters, behavioral traits, and return values beyond topic IDs. For a tool with undocumented inputs, this leaves significant gaps for the 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?

Schema description coverage is 0%, meaning none of the 4 parameters (topicId, includeVideos, year, limit) are documented in the schema. The description does not mention any parameters or their purposes, failing to compensate for the lack of schema documentation. This leaves the agent with no guidance on how to use the input fields effectively.

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 ('List') and resource ('all WWDC topic categories with their IDs'), distinguishing it from siblings like list_wwdc_videos or list_technologies by focusing on topic metadata rather than videos or technologies. It explicitly mentions the output format ('topic IDs like "swiftui-ui-frameworks"'), which adds clarity.

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 ('Essential first step before using list_wwdc_videos with topic filtering') and how its output integrates with other tools ('that can be used in other tools'), clearly differentiating it from alternatives like list_wwdc_videos or search_wwdc_content. This helps the agent understand its role in a workflow.

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 mentions what the tool finds (prerequisite sessions, follow-up content, similar talks) but doesn't describe response format, error conditions, rate limits, authentication needs, or whether it's read-only/destructive. For a 6-parameter tool with zero annotation coverage, this leaves significant behavioral gaps.

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 perfectly concise: two sentences with zero wasted words. The first sentence states the core purpose with specific details, and the second provides valuable context about use cases. Every element earns its place, and information is front-loaded appropriately.

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 6 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. While it states the tool's purpose concisely, it doesn't provide enough information about parameters, return values, or behavioral characteristics for effective tool use. The context signals indicate significant complexity that the description doesn't adequately address.

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 0%, so the description must compensate for all 6 parameters. It mentions 'specific video' (hinting at videoId/year) and types of relationships (hinting at include* parameters), but doesn't explain parameter meanings, the limit parameter, or required versus optional parameters. The description adds some semantic context but doesn't adequately compensate for the complete lack of schema descriptions.

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: 'Discover WWDC sessions related to a specific video' with specific types of relationships (prerequisite, follow-up, thematic). It distinguishes from siblings like 'get_wwdc_video' (single video) and 'list_wwdc_videos' (unfiltered list), though not all siblings are explicitly contrasted. The verb 'discover' and resource 'WWDC sessions' are specific, but sibling differentiation could be more explicit.

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 context ('Essential for creating learning paths') but doesn't explicitly state when to use this tool versus alternatives like 'search_wwdc_content' or 'browse_wwdc_topics'. It suggests the tool is for relationship discovery rather than general search, but lacks clear when-not-to-use guidance or named alternatives.

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 'Finds APIs' but doesn't describe how results are returned (e.g., list format, ranking, or pagination), potential rate limits, authentication needs, or error conditions. For a discovery tool with 4 parameters, this lack of behavioral context is a significant gap, 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 appropriately sized with two sentences that are front-loaded: the first sentence states the core purpose and key use cases, and the second provides a usage hint. There's no wasted text, and it efficiently conveys the tool's value, though it could be slightly more structured with bullet points for clarity.

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 complexity (4 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain parameter meanings, return values, or behavioral traits like result formatting or limitations. For a tool that likely returns structured API data, this leaves the agent with insufficient context to use it effectively beyond basic 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 schema description coverage is 0%, meaning none of the 4 parameters are documented in the schema. The description adds no information about parameters like 'apiUrl' (what format?), 'searchDepth' (what values?), 'filterByCategory' (what categories?), or 'includeAlternatives' (what does this toggle?). It fails to compensate for the schema's lack of descriptions, leaving parameters largely unexplained.

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: 'Discover alternative and related APIs' with specific examples like 'similar functionality, modern replacements for deprecated APIs, and platform-specific alternatives.' It distinguishes itself from siblings like 'get_related_apis' by emphasizing discovery and alternatives rather than just relatedness. However, it doesn't explicitly contrast with 'search_apple_docs' or 'search_framework_symbols,' leaving some sibling differentiation incomplete.

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 implied usage guidance with 'Perfect when looking for better ways to implement functionality,' which suggests it's for optimization or replacement scenarios. It doesn't explicitly state when not to use it or name alternatives among siblings, such as 'get_related_apis' for simpler relatedness or 'search_apple_docs' for broader searches. This leaves some ambiguity in tool selection.

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. While it mentions 'Supports enhanced analysis options for comprehensive API understanding,' it doesn't describe what the tool actually returns (e.g., full text, structured data, metadata), whether it requires authentication, rate limits, error handling, or how it differs from simply fetching a webpage. For a tool with 5 parameters and no output schema, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The subsequent sentences add useful context about usage timing and best use cases. While efficient, the mention of 'enhanced analysis options' could be more specific to justify its place, but overall it avoids unnecessary verbosity.

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 complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns, how parameters affect results, or provide enough behavioral context for a tool that presumably fetches and processes documentation content. The description covers purpose and basic usage but leaves too many gaps for effective agent 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?

Schema description coverage is 0%, so the description must compensate for all 5 parameters. The description mentions 'enhanced analysis options' and lists use cases like 'comprehensive API understanding,' but doesn't explain what any of the parameters (url, includeRelatedApis, includeReferences, includeSimilarApis, includePlatformAnalysis) actually do or how they affect the output. This leaves parameters semantically undocumented despite the high parameter count.

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: 'Get detailed content from a specific Apple Developer Documentation page.' It specifies the verb ('Get') and resource ('detailed content from a specific Apple Developer Documentation page'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this tool from its siblings like 'get_related_apis' or 'get_sample_code' beyond mentioning it's for 'full documentation' after using 'search_apple_docs'.

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: 'Use this after search_apple_docs to get full documentation' and 'Best for: reading API details, understanding usage, checking availability.' This gives explicit guidance on when to use the tool (after a search, for detailed API info) and its intended use cases. However, it doesn't specify when NOT to use it or name specific alternatives among the many sibling tools, which would be needed for a perfect score.

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 of behavioral disclosure. While it mentions what the tool tracks (updates, APIs, changes), it doesn't describe key behavioral traits such as whether this is a read-only operation, what format the output takes, whether it requires authentication, or if there are rate limits. For a tool with 6 parameters and no annotation coverage, this is a significant gap.

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 appropriately sized with three sentences. The first sentence states the core purpose, the second adds context, and the third provides usage guidance. It's front-loaded with the main function and avoids unnecessary elaboration, though the second sentence ('Essential for staying current...') is somewhat redundant.

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 complexity (6 parameters, no annotations, no output schema), the description is incomplete. It covers purpose and some usage guidelines but lacks behavioral transparency, parameter explanations, and output details. For a tool with this level of complexity and no structured support, the description should provide more comprehensive guidance to be effective.

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 0%, so the description must compensate for undocumented parameters. The description mentions categories like 'Apple platform updates, new APIs, and changes' and 'WWDC announcements, framework updates, and release notes,' which loosely relate to parameters like category and technology. However, it doesn't explain any of the 6 parameters (e.g., what category values are valid, what technology refers to, how searchQuery works), leaving most semantics unclear.

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: 'Track latest Apple platform updates, new APIs, and changes' with specific resources (WWDC announcements, framework updates, release notes). It distinguishes from some siblings by mentioning WWDC-specific tools, though not all alternatives are addressed. The verb 'track' is somewhat vague compared to more precise verbs like 'list' or 'retrieve'.

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 alternatives: 'For detailed WWDC videos, use WWDC-specific tools.' This distinguishes it from siblings like get_wwdc_video. However, it doesn't clarify when to use this tool versus other documentation-related siblings like search_apple_docs or get_apple_doc_content, leaving some ambiguity.

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 behavioral traits like checking 'minimum deployment targets, deprecations, and platform-specific features,' which adds context beyond basic functionality. However, it lacks details on rate limits, error handling, or response format, leaving gaps for a tool with no output schema.

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 appropriately sized and front-loaded, with a clear purpose statement followed by usage guidelines. Every sentence adds value, such as emphasizing criticality for cross-platform development and listing specific use cases, without redundancy.

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 complexity of checking API compatibility and the lack of annotations and output schema, the description is partially complete. It covers purpose and usage well but lacks details on parameters, return values, and behavioral constraints, making it insufficient for full agent 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?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'API availability' and 'platform alternatives,' which loosely relate to apiUrl and compareMode, but provides no specifics on parameter meanings, formats, or usage. With 3 parameters and no schema descriptions, this is inadequate.

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 specific verbs ('check', 'shows') and resources ('API availability across Apple platforms and OS versions'), distinguishing it from siblings like get_apple_doc_content or search_framework_symbols by focusing on compatibility analysis rather than documentation retrieval or 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 explicitly lists when to use the tool: 'planning app requirements, checking API availability, finding platform alternatives.' This provides clear context for usage without exclusions, helping the agent differentiate from siblings like get_sample_code or get_wwdc_video.

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 of behavioral disclosure. It mentions what the tool does (analyze relationships) but lacks details on permissions, rate limits, response format, or potential side effects. For a tool with 4 parameters and no output schema, this leaves significant gaps in understanding how it behaves.

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 front-loaded with the core purpose and structured with a 'Use when:' section, making it efficient. However, the sentence 'Essential for understanding how APIs work together' is somewhat redundant and could be omitted without loss of clarity, slightly reducing conciseness.

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 complexity of analyzing API relationships with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, parameter meanings, and return values, making it inadequate for an agent to fully understand how to invoke and interpret results from this 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 0%, so the description must compensate for undocumented parameters. It implies functionality related to inheritance, protocol conformances, and alternatives, which loosely maps to parameters like 'includeInherited' and 'includeConformance', but does not explicitly explain any parameters or their effects, leaving 4 parameters with unclear semantics.

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 analyzes API relationships and discovers related functionality, specifying inheritance, protocol conformances, and Apple's recommended alternatives. It distinguishes itself from siblings like 'find_similar_apis' by focusing on hierarchical and protocol-based relationships rather than similarity, though it doesn't explicitly contrast with all siblings.

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 contexts: 'learning API hierarchy, finding protocol requirements, discovering related functionality.' It gives clear guidance on when to use the tool but does not specify when not to use it or name alternatives among siblings, such as when to prefer 'find_similar_apis' for non-hierarchical relationships.

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 of behavioral disclosure. It describes the tool's function and scope but lacks details on behavioral traits such as rate limits, authentication needs, or response format. The description doesn't contradict annotations (none exist), but it doesn't fully compensate for the absence of structured behavioral data.

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 highly concise and front-loaded, consisting of three sentences that each add value: the first defines the purpose, the second differentiates from siblings, and the third provides usage guidance. There is no wasted text or redundancy.

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 (4 parameters, no annotations, no output schema), the description is partially complete. It excels in purpose and usage but lacks parameter semantics and behavioral details. Without an output schema, the description doesn't explain return values, leaving gaps in understanding how to interpret results.

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 description provides no information about the four parameters (framework, beta, searchQuery, limit). With 0% schema description coverage and no parameter details in the description, the agent has no semantic guidance beyond the parameter names. This is insufficient for a tool with multiple undocumented parameters.

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 specific verbs ('browse complete sample projects') and resources ('from Apple'), distinguishing it from sibling tools by explicitly contrasting with 'search_apple_docs which returns code snippets.' It specifies the scope as 'full working examples demonstrating best practices and implementation patterns.'

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 versus alternatives: 'Different from search_apple_docs which returns code snippets. Use for learning by example.' This clearly defines the context (learning by example) and names a specific sibling tool for comparison, helping the agent choose appropriately.

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 describes the type of content returned but doesn't mention critical behaviors: whether this is a read-only operation, how results are structured/paginated, authentication requirements, rate limits, or error conditions. The description adds some context about content types but lacks operational transparency needed for a tool with 5 parameters.

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 appropriately concise (3 sentences) and front-loaded with the core purpose. Each sentence adds value: first states the action and content scope, second elaborates on content types, third provides usage context. No wasted words, though it could be more structured with explicit parameter guidance.

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 5 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It adequately describes the tool's purpose and content focus but fails to address parameter semantics, behavioral expectations, or result formatting. For a tool with this complexity and lack of structured documentation, the description should provide more operational 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?

Schema description coverage is 0%, so the description must compensate for 5 undocumented parameters. The description mentions 'Apple technologies', 'frameworks', and content types but doesn't explain what the 5 parameters (category, platform, searchQuery, includeSubcategories, limit) mean or how they affect results. No parameter-specific guidance is provided beyond general content 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: 'Access comprehensive guides and tutorials for Apple technologies' with specific content types (guides, tutorials, overviews, best practices). It distinguishes from siblings like 'get_apple_doc_content' or 'search_apple_docs' by focusing on educational/learning materials rather than raw documentation or search. However, it doesn't explicitly contrast with 'list_technologies' or other learning-focused 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?

The description provides implied usage context: 'Perfect for learning new frameworks or understanding Apple's recommended approaches.' This suggests when to use it (educational purposes) but doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools. No guidance on parameter usage or prerequisites is provided.

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 mentions that results include 'code and its session context,' which adds some behavioral context. However, it lacks details on permissions, rate limits, pagination, or error handling, which are important for a tool with 5 parameters and no output schema.

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 appropriately sized and front-loaded, with two sentences that efficiently convey the purpose and result details. Every sentence adds value without redundancy or unnecessary elaboration.

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 complexity (5 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It doesn't explain parameters, return values, or behavioral traits like pagination or errors. For a tool with rich filtering options, this leaves too much unspecified.

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 0%, so the description must compensate for undocumented parameters. It doesn't explain any of the 5 parameters (framework, topic, year, language, limit), their formats, or how they affect filtering. This leaves significant gaps in understanding how to use the tool effectively.

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 ('browse') and resource ('code examples from WWDC sessions'), and it explains what the results include. However, it doesn't explicitly differentiate from siblings like 'get_sample_code' or 'search_wwdc_content', which might have overlapping 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 implies usage for 'finding implementation patterns, seeing new API usage, or learning by example,' which provides some context. But it doesn't specify when to use this tool versus alternatives like 'get_sample_code' or 'search_wwdc_content,' nor does it mention any exclusions or prerequisites.

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 provides 'offline access to entire session content', which is useful behavioral context about availability. However, it lacks details on permissions, rate limits, error handling, or response format, leaving 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 front-loaded with the core purpose in the first sentence, followed by usage guidance and a key behavioral trait. All three sentences earn their place by adding distinct value (content details, prerequisites, offline access), with zero waste or redundancy, making it efficiently 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 no annotations and no output schema, the description covers purpose and usage well but lacks details on behavioral aspects like response format, errors, or auth needs. For a tool with 4 parameters and 0% schema coverage, it should do more to explain parameters and expected outputs, leaving it adequate but with clear gaps.

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 0%, so the description must compensate for undocumented parameters. It mentions 'full transcript, code examples, and resources', which loosely maps to includeTranscript and includeCode parameters, but doesn't explain year or videoId or provide syntax/format details. With 4 parameters and low coverage, this adds minimal value beyond the schema.

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 ('Access complete WWDC session content') and resource ('WWDC session'), distinguishing it from siblings like list_wwdc_videos (which lists videos) or get_wwdc_code_examples (which focuses only on code). It specifies the content includes 'full transcript, code examples, and resources', making the purpose explicit and differentiated.

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 states when to use this tool: 'Use after finding videos with list_wwdc_videos or search_wwdc_content.' It provides clear prerequisites and context, guiding the agent to first identify videos using those siblings before accessing detailed content here, which is specific and actionable.

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 mentions 'browse' (implying read-only) and 'checking beta status,' which adds some behavioral context, but lacks details on permissions, rate limits, pagination, or response format. For a tool with no annotations, this is adequate but has gaps in behavioral disclosure.

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 front-loaded with the core purpose, followed by usage guidelines in a structured list. Every sentence earns its place by adding specific value without redundancy, making it efficient and well-organized.

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, 4 parameters with 0% schema coverage, and no output schema, the description does well by covering purpose, usage, and some parameter hints. However, it lacks details on response format or error handling, which are important for a tool with this complexity, leaving minor gaps in 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?

With 0% schema description coverage and 4 parameters, the description adds value by implying parameter usage: 'by category' hints at the 'category' parameter, 'checking beta status' relates to 'includeBeta,' and 'browse all' suggests 'limit' might control results. However, it doesn't fully document all parameters (e.g., 'language' is unmentioned), so it compensates partially but not completely.

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 specific verbs ('browse all Apple technologies and frameworks by category') and distinguishes it from siblings by mentioning its role in 'discovering available frameworks and understanding Apple's technology ecosystem.' It explicitly names a sibling tool ('search_framework_symbols') for differentiation.

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 guidelines with 'Use this when:' followed by three specific scenarios: exploring availability, finding framework identifiers for search_framework_symbols, and checking beta status. This clearly indicates when to use this tool versus alternatives, including a named sibling.

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 mentions 'full offline access to transcripts and code' which adds useful context about data availability, but doesn't cover important behavioral aspects like pagination (implied by 'limit' parameter but not explained), response format, error conditions, or whether this is a read-only operation (implied but not stated).

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 appropriately sized with three sentences that each add value: first states core functionality, second mentions filtering, third provides usage examples. It's front-loaded with the main purpose. No wasted words, though the second sentence could be more tightly integrated with the first.

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 4-parameter tool with no annotations and no output schema, the description provides adequate basic context about what the tool does and when to use it, but leaves significant gaps in parameter documentation and behavioral transparency. It covers the 'what' and 'why' reasonably well but lacks details on 'how' the parameters work and what to expect in return.

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?

With 0% schema description coverage for 4 parameters, the description must compensate but provides minimal parameter guidance. It mentions 'filtering options' generally and 'find sessions by topic' and 'identify videos with code examples' which loosely map to 'topic' and 'hasCode' parameters, but doesn't explain 'year' or 'limit' parameters at all. The description adds some meaning but insufficiently compensates for the schema coverage 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 purpose: 'Browse WWDC session videos with full offline access to transcripts and code.' It specifies the resource (WWDC session videos) and key features (offline transcripts/code access). However, it doesn't explicitly distinguish this from siblings like 'get_wwdc_video' or 'search_wwdc_content' beyond mentioning filtering options.

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 implied usage guidance: 'Use this to discover WWDC content, find sessions by topic, or identify videos with code examples.' This suggests when to use it (discovery, topic-based finding, code example identification) but doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling 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 discloses that the tool returns statistical data (counts and statistics) which is useful behavioral context. However, it doesn't mention potential limitations like data freshness, pagination, or error conditions that would be helpful for a read operation.

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 efficient sentences that front-load the core purpose ('List all available WWDC years') followed by specific details about what information is included. Every word contributes value with zero redundancy 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?

For a zero-parameter read tool with no annotations and no output schema, the description provides good coverage of what the tool does and what information it returns. However, it doesn't specify the format of the statistics or whether the list is sorted/limited, leaving some implementation details ambiguous.

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 0 parameters with 100% schema description coverage. The description appropriately doesn't discuss parameters since none exist. It earns a 4 rather than 5 because while it correctly omits parameter discussion, it doesn't explicitly state 'no parameters required' which would be optimal clarity.

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 'List' and resource 'all available WWDC years' with specific outputs 'video counts and statistics'. It distinguishes from siblings like 'list_wwdc_videos' by focusing on years rather than individual videos, and from 'browse_wwdc_topics' by focusing on temporal organization rather than thematic.

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 context: when you need to see which years have content and quantitative metrics about them. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings, though the distinction from 'list_wwdc_videos' is clear from the naming.

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 of behavioral disclosure. It mentions 'resolves all mentioned types, methods, and properties' but lacks details on how resolution works (e.g., depth, error handling, rate limits, or output format). For a tool with no annotations and 3 parameters, this is a significant gap in transparency about its operation and constraints.

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 front-loaded with the core purpose in the first sentence, followed by usage guidelines. It's concise with three sentences that each add value, though it could be slightly more structured (e.g., separating purpose from usage more clearly). No wasted words, but not perfectly optimized.

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, 0% schema coverage, 3 parameters, and no output schema, the description is incomplete. It explains the 'what' and 'when' but misses critical details like parameter meanings, behavioral traits (e.g., performance, errors), and expected outputs. For a tool of this complexity, it should provide more comprehensive 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?

Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description does not mention any parameters or their semantics (e.g., what 'sourceUrl' should contain, what 'maxReferences' limits, or how 'filterByType' works). It fails to compensate for the lack of schema documentation, leaving parameters ambiguous.

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: 'Deep dive into all types and APIs referenced in a documentation page' with specific verbs ('resolves', 'understand dependencies'). It distinguishes from siblings like 'get_apple_doc_content' (which fetches content) or 'search_framework_symbols' (which searches). However, it doesn't explicitly contrast with 'get_related_apis' or 'find_similar_apis', which might have overlapping 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 usage context: 'Use when: analyzing complex APIs, understanding type requirements, exploring API ecosystems.' This gives clear scenarios for when to invoke the tool. However, it doesn't specify when NOT to use it or mention alternatives among the sibling tools, such as when simpler lookups might suffice.

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 of behavioral disclosure. It describes the search functionality and scope but doesn't mention important behavioral aspects like rate limits, authentication requirements, result format, pagination, or error conditions. For a search tool with no annotation coverage, this leaves significant gaps.

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 efficiently structured with three sentences that each serve a distinct purpose: stating the tool's function, specifying its best use case, and providing alternative guidance. There's no wasted text, and the most important information (what the tool does) comes first.

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, no output schema, and 0% schema description coverage for a 2-parameter search tool, the description provides good purpose and usage guidance but lacks parameter details and behavioral context. It's adequate for basic understanding but incomplete for reliable tool 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?

Schema description coverage is 0%, meaning neither parameter has documentation in the schema. The description mentions searching for 'specific APIs, classes, or methods' which hints at the 'query' parameter's purpose, but provides no guidance on the 'type' parameter or any parameter syntax/format. It doesn't adequately compensate for the complete lack of schema documentation.

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 verb ('Search') and resource ('Apple Developer Documentation') with explicit scope ('APIs, frameworks, guides, and samples'). It distinguishes this tool from siblings by naming alternatives for different use cases (get_sample_code for sample code projects, WWDC tools for videos).

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 ('Best for finding specific APIs, classes, or methods') and when to use alternatives instead ('For browsing sample code projects, use get_sample_code. For WWDC videos, use the dedicated WWDC tools'). This directly addresses sibling 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 are provided, so the description carries the full burden. It describes the tool's function ('browse and search symbols') and scope ('within a specific Apple framework'), but lacks details on behavioral traits such as pagination, rate limits, authentication needs, or error handling. However, it does add context about the prerequisite (list_technologies), which is valuable.

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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines. Every sentence earns its place: the first defines the tool's function and use cases, and the second provides critical workflow context.

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 complexity (5 parameters, no annotations, no output schema), the description is incomplete. It covers purpose and usage well but lacks parameter explanations and behavioral details. However, it does provide essential context (prerequisite with list_technologies), making it minimally adequate but with clear gaps.

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 0%, so the description must compensate for all 5 parameters. It only mentions 'framework' implicitly (via 'specific Apple framework') and does not explain the semantics of symbolType, namePattern, language, or limit. This leaves most parameters undocumented, failing to add meaningful value beyond the bare schema.

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 specific verbs ('browse and search symbols') and resource ('within a specific Apple framework'), distinguishing it from siblings like list_technologies (which lists frameworks) and search_apple_docs (which searches documentation). It explicitly mentions exploring APIs, finding views/controllers/delegates, and discovering types, providing concrete use cases.

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: 'Use after list_technologies to get framework identifiers.' This clearly indicates a prerequisite and workflow context, helping the agent understand the proper sequence and alternative tools (e.g., use list_technologies first).

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 for behavioral disclosure. While it mentions the search capability and scope ('all WWDC video transcripts and code examples'), it doesn't describe important behavioral aspects like pagination, rate limits, authentication requirements, error handling, or what the search returns. For a search tool with 5 parameters and no annotations, this leaves significant gaps.

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 perfectly concise with three sentences that each earn their place: first establishes core functionality, second provides usage examples, third gives comparative guidance. It's front-loaded with the main purpose and wastes no 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 complexity (5 parameters, no output schema, no annotations), the description is incomplete. While it clearly states the purpose and provides comparative guidance, it lacks crucial information about parameter meanings, search behavior, result format, and limitations. For a search tool with multiple filtering parameters, this leaves too many unknowns 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?

With 0% schema description coverage for 5 parameters, the description must compensate but fails to do so. It mentions 'search across all WWDC video transcripts and code examples' which hints at scope but doesn't explain what the 5 parameters (query, searchIn, year, language, limit) mean or how they affect the search. The description adds minimal value beyond what the bare 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 performs 'full-text search across all WWDC video transcripts and code examples' with specific verbs ('search', 'find') and resources ('transcripts', 'code examples'). It explicitly distinguishes from sibling 'list_wwdc_videos' by stating it's 'more powerful... for finding specific content', providing clear differentiation.

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 versus alternatives: 'More powerful than list_wwdc_videos for finding specific content.' It also implies usage context through 'Find specific discussions, API mentions, or implementation examples,' giving clear scenarios where this tool is appropriate.

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