从CSV创建表格

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.

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.

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.

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.

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.

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.