Server Quality Checklist

Tool Scores

• suggest_utilitiesA
  3.6/5.0
  Behavior3/5

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

  No annotations are provided, so the description must carry the full burden of behavioral disclosure. It explains that the tool analyzes the prompt and recommends directly reusable methods, which is a safe, read-only operation implied. However, it does not disclose details such as the number of recommendations, what happens when no match is found, or any limitations. This is adequate but not rich.

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

  Conciseness4/5

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

  The description is a single block with a clear 'USE WHEN' header and a list of trigger phrases. It is not excessively long and front-loads the key usage context. The repetition of examples is somewhat redundant but does not harm clarity, so it earns a 4.

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

  Completeness4/5

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

  For a tool with one parameter and no output schema, the description provides sufficient context: it explains the purpose, usage, and trigger scenarios. It does not describe the return format or fallback behavior, but these are less critical given the simplicity of the tool. Overall, it is complete enough for an agent to invoke correctly.

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

  Parameters3/5

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

  The schema description coverage is 100%, and the parameter 'prompt' is well described with examples. The tool description itself does not add significant semantic value beyond what the schema already provides; it repeats similar examples. Thus, the baseline score of 3 is appropriate.

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

  Purpose4/5

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

  The description clearly states the tool's function: analyzing user logic requirements and recommending reusable methods from a utility library. It uses specific verbs like '推荐' and mentions resource ('工具方法库'), and provides multiple trigger examples. However, it doesn't explicitly distinguish itself from sibling tools like 'suggest_components' or 'query_utility', so it falls short of a 5.

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

  Usage Guidelines4/5

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

  The description provides explicit 'USE WHEN' guidance with concrete trigger scenarios (e.g., '实现数据格式化', '需要加密功能') and states it covers '所有逻辑开发任务'. This gives clear context for when to use the tool. It lacks explicit exclusions or alternatives, so it doesn't reach a 5.

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

• query_componentA
  4/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full transparency burden. It does disclose the type of information returned (props, events, slots, examples), but it doesn't mention side effects, permissions, or error behavior. The word 'query' implies read-only, providing some implicit reassurance, but this isn't explicitly stated.

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

  Conciseness5/5

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

  The description is two concise sentences, immediately front-loading the action ('query component name') and purpose ('understand specific usage'). Every word earns its place with no filler.

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

  Completeness4/5

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

  For a simple single-parameter query tool, the description adequately conveys the return content (props, events, slots, examples) and the use case. It doesn't address output formatting or error cases, but given the tool's simplicity and lack of output schema, these are minor gaps.

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

  Parameters3/5

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

  Schema coverage is 100% — the single parameter 'componentName' has a clear description with an example ('das-button'). The tool description only refers to 'component name' without adding extra semantic detail beyond what the schema already provides, so the baseline of 3 is appropriate.

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

  Purpose5/5

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

  The description clearly identifies the tool's function: querying detailed component information by name, and enumerates the specific fields returned (props, events, slots, usage examples). This distinguishes it from sibling tools like query_utility, which likely handles utilities.

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

  Usage Guidelines4/5

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

  It states the tool is used to understand the specific usage of recommended components, providing clear context for when to invoke it (after a component recommendation). However, it doesn't explicitly mention when not to use it or name direct alternatives.

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

• query_utilityA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the transparency burden. It discloses that the tool returns parameters, return values, and usage examples, and the verb '查询' (query) implies a read-only operation. However, it does not explicitly state safety (e.g., no side effects) or other behavioral details like error conditions or permission requirements.

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

  Conciseness5/5

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

  The description is two sentences, front-loaded with the core function in the first sentence and the use case in the second. Every word is purposeful; no filler or redundancy.

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

  Completeness4/5

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

  Given the tool's simplicity (1 parameter, no output schema) and the lack of annotations, the description adequately covers purpose, return content (parameters, return values, examples), and usage context. It could be more complete with explicit read-only confirmation or error handling notes, but for a query tool it is sufficient.

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

  Parameters3/5

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

  The schema covers 100% of the single parameter with a clear description and an example ('formatNumber'). The tool description adds little beyond the schema, merely restating the parameter's role ('根据工具方法名称'). This meets the baseline for high schema coverage.

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

  Purpose5/5

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

  The description clearly states the tool's function: '根据工具方法名称查询详细信息' (query detailed information based on utility method name). It specifies the verb (query), resource (utility method), and distinguishes from siblings like query_component (which targets components) and suggest_utilities (which suggests rather than queries).

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

  Usage Guidelines4/5

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

  The description provides clear context: '用于了解推荐工具方法的具体用法' (used to understand usage of recommended utility methods). This implies when to use it—when you have a recommended utility and need details. It doesn't explicitly mention exclusions or alternatives, but the context is unambiguous enough.

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

• suggest_componentsA
  4.1/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden of disclosing behavior. It reveals that the tool generates an optimized prompt (the output) and that it draws from a private component library. It does not mention side effects, permissions, or access constraints, but for a suggestion tool these are minimal. The disclosure of the output format adds relevant context.

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

  Conciseness4/5

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

  The description is front-loaded with 'USE WHEN' and is structured clearly, with trigger examples and a statement of the tool's behavior. It is slightly verbose due to the repeated examples of UI development tasks, but each element serves a purpose and there is no filler.

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

  Completeness4/5

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

  For a simple tool with one input parameter and no output schema, the description adequately covers when to use it, what it does, and what it returns (an optimized prompt). It does not explicitly explain how it differs from sibling tools beyond the focus on components, but the purpose and usage context are sufficiently clear.

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

  Parameters3/5

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

  The schema provides a detailed description for the single 'prompt' parameter with concrete examples ('generate a login page', 'create user list'), achieving 100% schema description coverage. The tool description does not add any parameter-specific meaning beyond what the schema already offers, so the baseline of 3 is appropriate.

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

  Purpose5/5

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

  The description clearly states the tool's purpose: it analyzes user requirements, recommends the most suitable components from a private library (@private-basic-components), and generates an optimized prompt with implementation suggestions. It uses a specific verb ('recommend'/'suggest') and resource, and the focus on components distinguishes it from siblings like suggest_utilities and query_component.

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

  Usage Guidelines4/5

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

  The description begins with 'USE WHEN' and lists explicit trigger scenarios (creating pages, components, interfaces, forms, etc.), providing clear usage context. However, it does not explicitly mention when not to use this tool or recommend alternatives (e.g., suggest_utilities for utility tasks), so it lacks full exclusion guidance.

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