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

With no annotations provided, the description carries full burden. It mentions support for version filtering and pagination, which are useful behavioral details. However, it doesn't disclose important traits like whether this is a read-only operation, what authentication might be required, rate limits, or what format the changelog data returns in. The description adds some context but 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 efficiently structured with two sentences: one stating the purpose and use cases, and another describing parameter support. Every sentence earns its place by providing essential information without redundancy. The use cases are presented in a clear numbered list format.

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 (3 parameters, no output schema, no annotations), the description provides good purpose and usage guidance but lacks important behavioral context. Without annotations or an output schema, the description should ideally explain more about what the tool returns and any operational constraints. It's adequate but has clear 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?

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description mentions '支持按版本号过滤和分页' (supports version filtering and pagination), which aligns with the 'version' and 'page' parameters but doesn't add meaningful semantic information beyond what the schema provides. The baseline of 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('查询' - query) and resources ('my-design 组件库的变更日志和迁移指南' - changelog and migration guide for my-design component library). It distinguishes from siblings by focusing on changelog queries rather than component details, examples, listings, searches, or theme tokens.

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 three use cases: 1) understanding version updates, 2) finding breaking changes and migration methods, and 3) troubleshooting version upgrade issues. This provides clear guidance on when to use this tool versus alternatives like component_search or component_details.

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 retrieved (documentation sections) and the mandatory nature of the call, but doesn't disclose behavioral traits like error handling, response format, authentication requirements, rate limits, or whether it's a read-only operation. The description adds some context about the tool's role but lacks operational details.

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?

Well-structured with two sentences: first lists what information is retrieved, second states when to use it. Every sentence earns its place by providing essential information. Could be slightly more concise by combining elements, but overall efficient and front-loaded with the core purpose.

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 documentation retrieval tool with no annotations and no output schema, the description provides good purpose and usage context but lacks information about what the response looks like, error conditions, or operational constraints. It's adequate for understanding when to use the tool but incomplete for understanding what to expect from the call.

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 both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it mentions the same sections (props, events, rules, etc.) but provides no additional syntax, format, or usage details. Baseline 3 is appropriate when 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 specific action ('获取' - get/retrieve) and resource ('my-design 组件的详细文档' - my-design component detailed documentation), listing exactly what information is included (Props, Events, Rules, Behavior, When to use, Accessibility). It distinguishes from siblings by focusing on comprehensive documentation retrieval rather than examples, lists, search, or changelogs.

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: '这是生成代码前必须调用的工具' (this tool must be called before generating code). It provides clear context about its mandatory nature in the development workflow and distinguishes from alternatives by specifying it's for confirming API and usage constraints before coding.

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. While it mentions the tool returns '可直接复制使用的示例代码' (ready-to-copy example code) and covers common scenarios, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error conditions, or whether this is a read-only operation. The description provides some context but leaves significant behavioral questions unanswered.

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 with two sentences that efficiently communicate the tool's purpose and value. The first sentence states the core function, and the second adds important context about the quality and scope of returned examples. There's no wasted verbiage, though it could be slightly more 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 single-parameter tool with good schema coverage but no annotations and no output schema, the description provides adequate but incomplete context. It explains what the tool returns (example code covering common scenarios) but doesn't address format, structure, or potential limitations of the output. Given the complexity level, it's minimally viable but leaves room for more complete guidance.

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

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

The schema description coverage is 100% with the single parameter 'componentName' well-documented in the schema. The description doesn't add any additional parameter semantics beyond what's already in the schema (which explains it accepts component names like Button, Input, Table and supports aliases). This meets the baseline score of 3 when schema coverage is high.

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 ('获取' - get/retrieve), resource ('my-design 组件的代码示例' - my-design component code examples), and scope ('覆盖组件的常见使用场景' - covering common usage scenarios). It distinguishes itself from siblings like component_details or component_search by focusing on ready-to-use example code rather than metadata or search 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 context by mentioning '可直接复制使用的示例代码' (ready-to-copy example code) and covering common scenarios, but doesn't explicitly state when to use this tool versus alternatives like component_details for documentation or component_search for discovery. No explicit exclusions or comparisons with sibling tools are 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool returns '所有可用组件及其分类、状态等元信息' (all available components with their classification, status, and other metadata), which gives some output context. However, it doesn't address important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or pagination behavior for large result sets.

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 with two sentences that directly address the tool's function and return value. It's front-loaded with the core purpose and efficiently communicates the scope of returned information without 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 tool's moderate complexity (list operation with optional filtering), lack of annotations, and absence of an output schema, the description provides basic but incomplete context. It covers what the tool does and what information it returns, but doesn't address behavioral aspects like safety, performance characteristics, or error conditions that would be important for an agent to use it effectively.

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

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

The schema description coverage is 100%, with both parameters (category and status) well-documented in the schema itself. The description doesn't add any parameter semantics beyond what's already in the schema - it doesn't explain the filtering logic, default behavior when parameters aren't provided, or how multiple filters interact. This meets the baseline expectation when schema coverage is complete.

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: '获取 my-design 组件库的组件列表' (get the component list of the my-design component library). It specifies the resource (component library) and action (get list), but doesn't distinguish it from sibling tools like component_search or component_details, which would require explicit 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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like component_search (which might offer more flexible filtering) or component_details (which provides detailed information about specific components), leaving the agent to guess about appropriate usage contexts.

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. It discloses the fuzzy search behavior and the multiple searchable attributes (name, alias, keywords, category), which is valuable. However, it doesn't mention important behavioral aspects like pagination, result limits, sorting, or what the response format looks like. For a search tool with no annotations, this leaves significant gaps in understanding how the tool actually 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 efficiently structured with a clear purpose statement followed by three specific use cases. Every sentence earns its place by providing distinct value: the first defines the tool's function, the second lists searchable attributes, and the third provides concrete usage scenarios. No wasted words 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 moderate complexity (search function with fuzzy matching), no annotations, and no output schema, the description is adequate but incomplete. It covers the purpose and usage guidelines well, but lacks information about behavioral aspects (pagination, limits, response format) that would be important for an agent to use this tool effectively. The description does its job for basic understanding but leaves gaps in operational details.

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 single 'query' parameter well-documented in the schema. The description adds some context by listing the types of content that can be searched (component name, alias, keywords, category) and providing example queries, but this mostly reinforces what's already in the schema. The baseline of 3 is appropriate since the schema does the heavy lifting for parameter 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 action ('搜索' - search) and resource ('my-design 组件库的组件' - my-design component library components). It distinguishes this tool from siblings by specifying it's for fuzzy search across multiple attributes (name, alias, keywords, category), unlike component_list which likely lists all components or component_details which shows details for a specific component.

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

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

The description explicitly provides three use cases: 1) exploring when uncertain about component names, 2) finding suitable components based on requirements, and 3) finding related components. This gives clear guidance on when to use this tool versus alternatives like component_list (likely for browsing all components) or component_details (for detailed information on a known component).

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 what the tool returns (tokens and theme info) and a usage recommendation for code generation, but it lacks critical behavioral details such as whether this is a read-only operation, potential rate limits, authentication requirements, error handling, or the format/structure of the returned data. For a tool with no annotations, this is a significant gap in transparency.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose. It uses two sentences: one for the tool's function and one for usage advice. There's minimal waste, though the second sentence could be seen as slightly extraneous if the focus is purely on tool invocation rather than code generation best practices.

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 (2 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and includes a usage tip, but it lacks details on behavioral aspects (e.g., read/write nature, data format) and doesn't compensate for the absence of an output schema. This makes it adequate for basic understanding but insufficient for full agent confidence without additional context.

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

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

The input schema has 100% description coverage, with clear documentation for both parameters ('type' and 'theme'), including optional values and defaults. The description adds no additional parameter semantics beyond what's in the schema; it doesn't explain parameter interactions, provide examples, or clarify edge cases. According to the rules, with high schema coverage (>80%), the baseline is 3 even without param info in the 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: '获取 my-design 的 Design Token（设计令牌）和主题信息' (Get Design Tokens and theme information for my-design). It specifies the resources (color, spacing, radius, font, shadow tokens) and the scope (light/dark theme differences). However, it doesn't explicitly distinguish this tool from its siblings (like component_details or component_list), which might also involve design-related data, so it doesn't achieve full sibling 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 implied usage guidance by stating '生成代码时应优先使用 token（CSS 变量），避免硬编码颜色、间距等值' (When generating code, prioritize using tokens (CSS variables) and avoid hardcoding values like colors and spacing). This suggests the tool is for code generation contexts. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., vs. component_details for component-specific tokens) or provide clear exclusions, leaving some ambiguity.

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