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 the tool is 'much more focused than browsing all products', which adds useful context about scope, but lacks details on behavioral traits like pagination, rate limits, error handling, or response format. For a read operation with no annotations, this is a moderate gap, but the description does add some value beyond the basic purpose.

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 concise and well-structured in three sentences: the first states the purpose, the second provides usage guidelines with examples, and the third differentiates from alternatives. Each sentence adds value without redundancy, making it front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is fairly complete. It covers purpose, usage, and differentiation, but lacks details on output (e.g., what data is returned, format) and behavioral aspects like error cases. For a simple read tool, this is adequate but not fully comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the parameter 'category' fully documented in the schema (including examples like 'os', 'lang'). The description does not add any parameter-specific information beyond what the schema provides, such as format constraints or usage tips. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to.

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 ('Find') and resource ('all products within a specific category'), making the purpose explicit. It distinguishes from siblings by emphasizing focus on a specific category versus broader tools like 'list_products' or 'list_categories', and differentiates from 'get_product' (single product) and 'get_tag_products' (tag-based filtering).

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: for 'comparative analysis' or when users ask 'broad questions' about categories. It also states when not to use it by contrasting with 'browsing all products' (likely referring to 'list_products'), making alternatives clear. The examples ('What databases are tracked?', 'Show me all the operating systems.') further illustrate appropriate use cases.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns ('all release cycles, support dates, and EOL status') and implies it's a read-only operation through the verb 'Get', but does not mention potential limitations like rate limits, authentication needs, or error handling.

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 first sentence stating the core purpose and the second providing usage examples. Every sentence adds value without redundancy, making it efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is largely complete—it explains what the tool does and provides usage examples. However, it could improve by briefly mentioning the return format or data structure, as there's no output schema to clarify this.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the schema already documents the single parameter thoroughly. The description does not add any additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get comprehensive end-of-life information') and resource ('for a specific software product'), distinguishing it from siblings like list_products or get_category_products by focusing on detailed product-level data rather than listing or categorical operations.

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 this tool through concrete examples ('When does Python 3.8 reach end-of-life?' or 'What Ubuntu versions are still supported?'), but does not explicitly state when not to use it or name specific alternatives among the 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 describes what information is returned (end-of-life dates, support status, patch version) which is helpful behavioral context. However, it doesn't mention error conditions, rate limits, authentication requirements, or what happens with invalid product/release parameters, leaving some behavioral aspects unclear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that each earn their place - the first states the purpose, the second provides usage guidance with specific examples. No wasted words, front-loaded with the core purpose, and 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?

For a read-only tool with 2 parameters and 100% schema coverage but no output schema, the description provides good context about what information is returned. However, without an output schema, it could benefit from more detail about the structure of returned data. The description compensates well with specific examples of returned information types.

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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions, maintaining the baseline score of 3 for adequate parameter documentation through the schema alone.

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 'get' and resource 'detailed information about a specific version/release of a product', with specific examples of what information is retrieved (end-of-life dates, support status, patch version). It distinguishes from sibling tools like get_product (which likely gets general product info) by focusing on version-specific details.

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 'Use this when you need precise details about one particular version', providing clear when-to-use guidance. It also distinguishes from alternatives by specifying this is for 'specific version/release' details rather than general product information available from sibling tools like get_product or list_products.

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 the tool is 'useful for discovering related technologies' but doesn't specify behavioral traits such as whether it returns a list or single result, pagination, error handling, or performance characteristics. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond its basic purpose.

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: the first sentence states the core purpose, and the second adds usage context without redundancy. Every sentence earns its place by providing distinct value, making it efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers purpose and usage well but lacks details on return values, error cases, or behavioral nuances. Without an output schema, the description should ideally hint at what's returned, but it doesn't, leaving the agent uncertain about the result format.

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 adds some meaning beyond the input schema by explaining that tags 'often represent vendors (e.g., 'microsoft', 'google'), technology types, or other groupings,' which provides context for the 'tag' parameter. However, with 100% schema description coverage (the schema already describes the tag parameter well), the description doesn't significantly enhance parameter understanding. This meets the baseline of 3 for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Find all products that share a common tag.' It specifies the verb ('find') and resource ('products'), and distinguishes it from siblings by focusing on tag-based filtering rather than category-based (get_category_products) or individual product retrieval (get_product). However, it doesn't explicitly contrast with list_products, which might also list products, making it slightly less specific than a perfect 5.

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 this tool: 'Useful for discovering related technologies or when users want to see all products from a specific vendor or with particular characteristics.' This implicitly suggests alternatives like get_category_products for category-based grouping or list_products for unfiltered listing, but it doesn't explicitly name when-not-to-use cases or directly compare to siblings, so it falls short of a perfect 5.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a list of categories with examples ('os', 'lang', etc.), which is useful behavioral context. However, it doesn't mention potential limitations like pagination, rate limits, authentication needs, or error conditions. For a read-only tool with no parameters, this is adequate but not comprehensive.

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: the first states the purpose and output, the second provides usage guidance. It's front-loaded with key information and avoids redundancy. However, the second sentence could be slightly more concise (e.g., 'Use for taxonomy exploration or to help users find related products').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is reasonably complete. It explains what the tool does and provides example outputs, which helps compensate for the missing output schema. However, it lacks details on return format (e.g., list structure, data types) and doesn't address potential edge cases, leaving some gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details beyond the schema, but that's appropriate here. Baseline is 4 for 0 parameters, as the description focuses on output semantics instead, which is valuable given the lack of an output 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: 'Discover how products are organized by category' and 'Returns categories like...' which specifies the verb (discover/return) and resource (categories). It distinguishes from siblings like list_products or get_category_products by focusing on category taxonomy rather than products themselves. However, it doesn't explicitly contrast with all siblings (e.g., list_tags).

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 understand the taxonomy or to help users explore related products.' This suggests contexts where category exploration is needed. However, it doesn't explicitly state when to use this versus alternatives like get_category_products (which might fetch products within a category) or list_tags (another organizational scheme), nor does it mention any prerequisites or exclusions.

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

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits: it's a read operation ('Browse', 'Returns'), implies no destructive actions, and describes the return content (product names, descriptions, basic metadata). However, it lacks details on rate limits, authentication needs, or pagination behavior, which are common gaps for list tools.

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: the first sentence states the core purpose, followed by usage guidelines and return details. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 optional parameter, no output schema), the description is reasonably complete. It covers purpose, usage, and return values, compensating for the lack of annotations. However, it could improve by mentioning sibling tools explicitly or detailing error cases, keeping it from a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'full' parameter well-documented in the schema itself. The description adds no parameter-specific information beyond what the schema provides, such as clarifying the 'full' flag's impact on performance or use cases. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Browse all software products tracked for end-of-life information' (specific verb+resource). It distinguishes itself from siblings by focusing on comprehensive listing rather than filtered queries (e.g., get_category_products, get_product). However, it doesn't explicitly contrast with all siblings like list_categories or list_tags, keeping it from a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: 'Use this as your starting point to discover what products are available, or when you need to help users find software they're asking about.' This indicates when to use it (initial discovery, user assistance) but doesn't explicitly state when not to use it or name specific alternatives among siblings, such as get_product for detailed single-product queries.

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 that tags describe 'technical attributes, company associations, or use cases' which adds some context about tag content, but fails to describe what the tool actually returns (e.g., a list of tag names, tag metadata, or products with tags), any pagination, sorting, or filtering behavior, or system 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 two sentences that efficiently explain the tool's purpose and differentiate tags from categories. It avoids redundancy and is appropriately front-loaded with the core function. Minor improvement possible by specifying the return type.

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 zero parameters, no annotations, and no output schema, the description is incomplete. It explains the conceptual role of tags but doesn't specify what the tool returns (e.g., a list of tag strings, tag objects with counts, or something else), leaving the agent uncertain about the output format. For a tool with no structured metadata, more behavioral detail is needed.

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 with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's conceptual purpose. This meets the baseline expectation for parameterless tools.

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 states the tool's purpose is to 'explore products by tags/labels' which is clear but vague about the specific action. It distinguishes tags from categories but doesn't specify if this lists tags themselves, products filtered by tags, or something else. Compared to sibling 'list_categories' and 'get_tag_products', the differentiation is 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 explains what tags are and how they differ from categories, providing some context about when tags might be relevant. However, it offers no explicit guidance on when to use this tool versus alternatives like 'get_tag_products' or 'list_products', nor any prerequisites or exclusions for usage.

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