lanhu_get_dds_schema

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns AI-recognized UI component structure with row/col layout and precise styles. There is no mention of side effects or destructive actions, which is appropriate for a read-like operation. The description adds value beyond 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.

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

The description is two sentences: first introduces the tool, second explains the output and its use case. Every sentence adds value with no extraneous information. Front-loaded with key purpose.

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 simplicity of the tool (2 params, no output schema, no annotations), the description is complete. It explains what the tool returns (component tree, layout, styles) and the use context (AI-recognized structure, precise restoration). No gaps.

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 100%, so baseline is 3. The description does not add extra information about the parameters beyond what is in the schema ('imageId' and 'versionId'). No elaboration on format or constraints is provided.

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 uses a specific verb ('获取') and resource ('蓝湖 DDS 语义化 UI 组件树'), provides examples of recognized components (NavBar/Avatar/Input/ImageText), and mentions layout and styles. It clearly distinguishes this tool from siblings like lanhu_get_annotations or lanhu_get_tokens.

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 states the tool is for '300% 精确还原设计稿' (300% accurate design restoration), implying its usage when precise UI reproduction is needed. While it does not explicitly list when-not-to-use or alternatives, the distinct purpose is clear among sibling tools.

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