Server Quality Checklist

Tool Scores

• create_table_from_csvB
  3.1/5.0
  Behavior2/5

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

  No annotations are provided, so the description must fully disclose behavioral traits. It states the tool converts CSV data to table data for documents, implying a transformation or creation operation, but lacks details on permissions, side effects, error handling, or output format. While the output schema exists, the description doesn't add behavioral context beyond the basic function, leaving gaps in understanding how the tool behaves in practice.

  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 a single, efficient sentence: '将CSV数据转换为可用于文档的表格数据'. It is front-loaded with the core purpose, has no redundant words, and is appropriately sized for the tool's complexity. Every part of the sentence contributes to understanding the tool's function.

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

  Completeness3/5

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

  Given the tool's moderate complexity (4 parameters, no annotations, but with an output schema), the description is minimally adequate. It states what the tool does but lacks behavioral details, usage guidelines, and parameter insights. The presence of an output schema means return values are documented elsewhere, so the description doesn't need to cover that. However, for a tool with no annotations, more context on behavior and usage would improve completeness.

  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 description does not mention any parameters, focusing only on the overall function. However, the input schema has 100% description coverage, with clear documentation for all four parameters (csvData, hasHeader, delimiter, styleName). Since the schema provides comprehensive parameter information, the baseline score is 3, as the description adds no extra semantic value but doesn't need to compensate for schema gaps.

  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 purpose: '将CSV数据转换为可用于文档的表格数据' (Convert CSV data into table data that can be used in documents). It specifies the verb 'convert' and the resource 'CSV data', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'create_table_from_json', which would be needed for a score of 5.

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

  Usage Guidelines2/5

  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 such as 'create_table_from_json' or 'list_table_styles', nor does it specify prerequisites or contexts for usage. This lack of comparative or contextual information limits its helpfulness for an AI agent.

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

• create_table_from_jsonB
  3.1/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the conversion action but doesn't describe what happens during processing: whether the transformation preserves all data, how errors are handled, what the output format looks like, or any performance characteristics. For a data transformation tool with zero annotation coverage, this 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.

  Conciseness5/5

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

  The description is a single, efficient sentence that directly states the tool's core function without unnecessary words. It's appropriately sized for a straightforward data transformation tool and gets straight to the point about what the tool accomplishes.

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

  Completeness3/5

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

  Given that an output schema exists, the description doesn't need to explain return values. However, for a data transformation tool with no annotations, the description should provide more context about the transformation process, error handling, and output characteristics. The current description is minimal but functional - adequate for the basic purpose but lacking depth for confident usage.

  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%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it mentions JSON array data generally but doesn't elaborate on format requirements, validation rules, or usage examples. With complete schema documentation, 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 purpose: converting JSON array data into table data for documents. It specifies both the input (JSON array) and output (table data), making the verb+resource relationship explicit. However, it doesn't distinguish this tool from its sibling 'create_table_from_csv', which performs a similar conversion from a different data format.

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

  Usage Guidelines2/5

  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. There's no mention of when this tool is appropriate versus 'create_table_from_csv' for CSV data, 'list_table_styles' for style information, or 'markdown_to_docx' for document conversion. The description only states what the tool does, not when to choose it.

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

• list_table_stylesB
  3.2/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries full burden. It states this is a retrieval operation ('获取'), implying it's likely read-only and non-destructive, but doesn't explicitly confirm safety aspects. It doesn't disclose behavioral traits like rate limits, authentication needs, response format, or whether it returns all styles at once. The description adds minimal 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.

  Conciseness5/5

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

  The description is a single, efficient sentence in Chinese that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place.

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

  Completeness3/5

  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, output schema exists), the description is adequate but minimal. It covers the basic purpose but lacks context about the returned data (e.g., format, scope of 'all') and behavioral details. With no annotations and an output schema, the description should ideally hint at what the output contains, but it doesn't. It meets minimum viability for a simple retrieval tool.

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

  Parameters4/5

  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, which is appropriate here. A baseline of 4 is applied for tools with zero parameters, as the description correctly avoids unnecessary parameter explanations.

  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 purpose with a specific verb ('获取' meaning 'get') and resource ('预定义表格样式' meaning 'predefined table styles'). It distinguishes this as a retrieval operation rather than a creation or conversion tool like its siblings (create_table_from_csv, create_table_from_json, markdown_to_docx). However, it doesn't explicitly differentiate from potential similar 'get' operations that might exist elsewhere.

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

  Usage Guidelines2/5

  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 prerequisites, timing considerations, or how it relates to sibling tools (e.g., whether these styles are used by the creation tools). The agent must infer usage from the purpose alone.

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

• markdown_to_docxB
  3.3/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions support for style configuration, templates, and image embedding, but lacks critical behavioral details such as whether this is a read-only or destructive operation, error handling, performance characteristics, or output location behavior. For a complex conversion tool, 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.

  Conciseness5/5

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

  The description is extremely concise - a single sentence that efficiently communicates the core functionality and key features. Every word earns its place, with no redundant information. The structure is front-loaded with the primary purpose followed by supporting features.

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

  Completeness3/5

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

  Given the tool's complexity (6 parameters, nested objects) and the presence of an output schema, the description is moderately complete. It covers the basic conversion purpose and mentions key features, but lacks guidance on usage scenarios, behavioral constraints, and parameter interactions. The output schema reduces the need to describe return values, but more context about the tool's operation would be helpful.

  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 description adds minimal parameter semantics beyond the schema. It mentions 'style configuration, template system, and multiple image embedding methods' which loosely maps to the 'styleConfig', 'template', and image handling capabilities, but doesn't explain parameter interactions or provide usage examples. With 100% schema description coverage, the baseline is 3 as the schema does most of the documentation work.

  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 specific verb ('convert') and resource ('Markdown document to Word document'), specifying the output format (DOCX). It distinguishes from siblings by focusing on document conversion rather than table creation or listing operations, making the purpose unambiguous and well-defined.

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

  Usage Guidelines2/5

  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 does not mention prerequisites, constraints, or scenarios where this conversion is appropriate compared to other document processing tools. Usage context is implied but not explicitly stated.

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