ListDataQualityResults

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states it's a query/list operation, but doesn't disclose whether this is paginated (though parameters suggest it is), what permissions are required, rate limits, what format results are returned in, or whether this is a read-only operation. For a tool with 10 parameters and no annotations, this is a significant gap in behavioral context.

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 - a single Chinese phrase. While this might seem efficient, it's actually under-specified rather than appropriately concise. However, it doesn't waste words or have unnecessary structure. Given the scoring framework, this earns a 4 for being front-loaded and not verbose, though it borders on being too minimal.

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?

For a tool with 10 parameters, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what 'quality results' are, what the tool returns, how results are structured, or when to use it versus similar sibling tools. The minimal description fails to provide the necessary context for an AI agent to understand when and how to use this tool effectively.

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 input schema has 100% description coverage, with all 10 parameters well-documented in Chinese. The tool description adds no parameter information beyond what's already in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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 '查询质量结果列表' (Query quality results list) is a tautology that essentially restates the tool name 'ListDataQualityResults' in Chinese. It doesn't specify what 'quality results' refer to (data quality evaluation results), what resource is being listed, or how this differs from sibling tools like ListDataQualityEvaluationTasks or ListDataQualityRules. The purpose is vague and lacks specific verb+resource differentiation.

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 absolutely no guidance on when to use this tool versus alternatives. With many sibling tools related to data quality (ListDataQualityEvaluationTasks, ListDataQualityRules, ListDataQualityEvaluationTaskInstances, GetDataQualityEvaluationTaskInstance), there's no indication of when this specific listing tool is appropriate versus those other options. No context, exclusions, or prerequisites are mentioned.

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