Server Quality Checklist

Tool Scores

• convert_to_htmlA
  4/5.0
  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 and discloses key behavioral traits: it preserves original styling, supports multi-sheet files with selective conversion, handles large files via pagination, and returns structured JSON with status and summary. It does not mention error handling or performance limits, but covers core functionality well.

  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 front-loaded with the core purpose, followed by key features and return format in a single, efficient sentence. Every phrase adds value (e.g., 'preserve original style', 'supports multi-sheet files', 'returns structured JSON') with zero waste.

  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 complexity (6 parameters, file conversion with formatting) and no output schema, the description is mostly complete: it covers the conversion process, formatting preservation, multi-sheet support, pagination for large files, and JSON return structure. It lacks details on error cases or output schema specifics, but is sufficient for agent use.

  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 description coverage is 100%, so the schema fully documents all 6 parameters. The description adds no additional parameter semantics beyond what's in the schema, such as format details or usage examples. Baseline 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.

  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 with specific verbs ('convert', 'preserve', 'return') and resources ('Excel/CSV files', 'HTML files'), distinguishing it from siblings like 'apply_changes' and 'parse_sheet' by focusing on format conversion rather than data manipulation or parsing.

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

  Usage Guidelines3/5

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

  The description implies usage for converting spreadsheet files to HTML with formatting preservation, but does not explicitly state when to use this tool versus alternatives like 'parse_sheet' or provide exclusions (e.g., non-tabular data). It mentions support for large files and pagination as contextual features.

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

• apply_changesA
  4.2/5.0
  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 behavioral disclosure. It effectively describes key behavioral traits: it preserves original file formatting/styles, creates backup files by default to prevent data loss, and supports specific operations (add, delete, modify rows and cell data). This covers important mutation behavior, safety mechanisms, and operational scope that aren't in the schema.

  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 efficiently structured in three sentences: purpose statement, input requirements, and behavioral details. Every sentence adds value - no repetition or fluff. It's appropriately sized for a tool with 3 parameters and important behavioral characteristics.

  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 mutation tool with no annotations and no output schema, the description does well by explaining the write operation, safety mechanisms (backups), and supported edit types. It could be more complete by mentioning error conditions or what happens if the file_path doesn't exist, but it covers the essential context given the tool's complexity.

  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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal parameter semantics beyond the schema - it mentions the JSON format comes from 'parse_sheet' (which is also in the schema) and implies the backup default is true. This meets the baseline expectation when schema coverage is high.

  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 action ('将修改后的数据写回Excel/CSV文件' - write modified data back to Excel/CSV files) and resource (Excel/CSV files). It distinguishes from sibling tools like 'parse_sheet' (which reads data) and 'convert_to_html' (which converts format) by focusing on the write-back operation to complete the data editing loop.

  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 about when to use this tool: after modifying data from 'parse_sheet' to complete the editing workflow. It mentions the input format requirement ('接受parse_sheet返回的JSON格式数据'), establishing a prerequisite relationship. However, it doesn't explicitly state when NOT to use it or compare with alternatives beyond the implied workflow.

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

• parse_sheetA
  4.2/5.0
  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 behavioral disclosure. It effectively describes key behaviors: default returns overview info to avoid context overload, mentions performance considerations for large files, and hints at output structure (概览信息 like row count, column count, data types, preview). However, it doesn't detail error handling, file size limits, or exact JSON schema, leaving some gaps.

  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 concise and well-structured: it starts with the core purpose, explains default behavior and rationale, mentions parameter control, and ends with usage context. Every sentence adds value without redundancy, and it's front-loaded with essential information.

  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 complexity (7 parameters, no annotations, no output schema), the description does a good job of covering purpose, behavior, and usage. It mentions the output includes overview info and previews, compensating for the lack of output schema. However, it doesn't fully detail error cases or exact return formats, leaving some ambiguity for an agent.

  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 description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds some context by mentioning 'LLM可通过参数控制是否获取完整数据、样式信息等' (LLM can use parameters to control whether to get full data, style info, etc.), which reinforces parameter purposes, but doesn't provide significant additional semantics beyond what's in the schema. Baseline 3 is appropriate given 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 purpose: '解析Excel/CSV文件为结构化JSON数据' (parse Excel/CSV files into structured JSON data). It specifies the resource (Excel/CSV files) and the action (parse into JSON), and distinguishes it from sibling tools by mentioning '修改后可用apply_changes写回' (after modification, can use apply_changes to write back), showing it's for reading/parsing rather than conversion or writing.

  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 on when to use it: '适合数据分析和处理' (suitable for data analysis and processing). It mentions an alternative tool ('apply_changes') for writing back modifications, but doesn't explicitly state when not to use it or compare it to 'convert_to_html'. The guidance is helpful but not fully comprehensive regarding all sibling tools.

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