AttachDataQualityRulesToEvaluationTask

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

No annotations are provided, so the description carries full burden. It implies a mutation ('associate'), but doesn't disclose behavioral traits such as required permissions, whether the association is reversible, error handling, or side effects. For a tool that likely modifies configurations, this lack of transparency is a significant gap.

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 with no wasted words. It's front-loaded with the core action, though it could be more specific. The structure is straightforward, but the brevity contributes to vagueness rather than 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 complexity (3 parameters, mutation implied, no output schema), the description is incomplete. It lacks details on behavior, output, error cases, and how it fits with siblings like CreateDataQualityEvaluationTask. Without annotations or output schema, the description should provide more context to guide effective use.

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%, with clear parameter descriptions in Chinese. The tool description adds no additional meaning beyond the schema, such as explaining relationships between parameters or usage examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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 '关联数据质量规则与数据质量监控' translates to 'Associate data quality rules with data quality monitoring', which states the tool's purpose but is vague. It doesn't specify what 'associate' means (e.g., attach, link, configure) or clarify the relationship between rules and monitoring tasks. Compared to siblings like CreateDataQualityEvaluationTask or CreateDataQualityRule, it lacks differentiation in action specificity.

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 prerequisites (e.g., existing rules or tasks), exclusions, or related tools like UpdateDataQualityEvaluationTask. Without context, an agent must infer usage from the tool name and parameters alone.

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