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

With no annotations provided, the description carries full burden. It mentions the tool converts code to images and returns a dictionary with data and metadata, but lacks details on error handling, performance (e.g., rate limits), authentication needs, or side effects. This is inadequate for a mutation tool with zero annotation coverage.

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: the first sentence states the core purpose, followed by a structured list of parameters and return value. Every sentence earns its place with no redundant information, making it efficient and well-organized.

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 complexity (6 parameters, mutation operation) and no annotations, the description does well by detailing all parameters and noting the return structure. However, it lacks behavioral context like error cases or limitations. The presence of an output schema mitigates some gaps, but more completeness is needed for a mutation tool.

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 0%, so the description must compensate. It provides detailed semantics for all 6 parameters beyond the schema, including explanations of mermaid_code, output_format options, theme options, background_color syntax, and optional width/height. This adds significant value over the bare schema.

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 ('将 Mermaid 图表代码转换为多种格式的图像') with the resource (Mermaid chart code) and distinguishes from siblings by focusing on conversion rather than validation or option retrieval. It explicitly lists the output formats, making the purpose unambiguous.

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 by specifying what the tool does, but does not explicitly state when to use it versus alternatives like validate_mermaid_syntax or get_supported_options. No guidance on prerequisites or exclusions is provided, leaving usage context partially inferred.

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. It discloses that the tool returns a dictionary with lists of supported themes and formats, which adds behavioral context beyond the input schema (which has no parameters). However, it doesn't cover other traits like performance, error handling, or authentication needs, leaving gaps in transparency for a tool with no annotation support.

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 highly concise and well-structured: two sentences that directly state the purpose and return value, with no wasted words. It's front-loaded with the core function, and every sentence adds essential information, making it efficient for an agent to parse.

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 low complexity (0 parameters, no annotations, but with an output schema), the description is reasonably complete. It explains what the tool does and the return format, which complements the output schema. However, it lacks usage context and some behavioral details, preventing a perfect score despite the structured support.

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 tool has 0 parameters, and the input schema description coverage is 100% (with an empty schema). The description doesn't need to add parameter semantics, so it appropriately focuses on the return value. Since there are no parameters to document, a baseline score of 4 is justified, as the description doesn't introduce confusion or redundancy.

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: '获取转换器支持的选项，如图表主题和输出格式' (Get converter-supported options, such as chart themes and output formats). It specifies both the action ('获取' - get) and the resource ('支持的选项' - supported options), with concrete examples. However, it doesn't explicitly differentiate from sibling tools like 'convert_mermaid_to_image' or 'validate_mermaid_syntax', which prevents 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 or suggest scenarios where this tool is appropriate (e.g., before conversion to check available options). Without any usage context or exclusions, it relies on implicit understanding, which is insufficient for clear agent decision-making.

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 the full burden of behavioral disclosure. It mentions '尝试简单转换' (attempting simple conversion) as the validation method, which implies a read-only, non-destructive operation, but doesn't clarify error handling, performance implications, or what '简单转换' entails. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 extremely concise and well-structured: a purpose statement followed by clear parameter and return sections in bullet-like format. Every sentence earns its place without redundancy, and it's front-loaded with the core functionality. The bilingual presentation (Chinese purpose, English labels) is efficient for clarity.

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 low complexity (single parameter, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameter semantics, and return type at a high level. However, it lacks usage guidelines and detailed behavioral context, which are minor gaps in this simple validation 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 description explicitly documents the single parameter 'mermaid_code' as '要验证的 Mermaid 图表语法代码' (Mermaid diagram syntax code to validate), adding meaning beyond the schema's basic title 'Mermaid Code'. However, with schema description coverage at 0%, it doesn't provide format details, constraints, or examples. The baseline is 3 since it compensates somewhat but not fully for the schema's lack of descriptions.

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 as '验证 Mermaid 图表语法' (validate Mermaid diagram syntax) and specifies the method '通过尝试简单转换' (by attempting simple conversion). It distinguishes from sibling tools like 'convert_mermaid_to_image' by focusing on validation rather than conversion to image format. However, it doesn't explicitly differentiate from 'get_supported_options' which might relate to syntax options.

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 'convert_mermaid_to_image' or 'get_supported_options', nor does it specify scenarios where validation is preferred over direct conversion or option checking. There's no indication of prerequisites or exclusions for usage.

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