UpdateDataQualityRule

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

No annotations are provided, so the description carries full burden for behavioral disclosure. The description only states '更新质量规则' (Update quality rule) which implies a mutation operation but provides no information about permissions required, whether the update is partial or complete, what happens to existing configurations, error handling, rate limits, or what the response looks like. For a complex mutation tool with 10 parameters and nested objects, this complete lack of behavioral context is inadequate.

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 consists of a single phrase '更新质量规则' (Update quality rule) which is extremely concise with zero wasted words. While this under-specifies the tool's functionality, it's not verbose or poorly structured. Every word earns its place, though more content would be needed for adequacy.

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 (10 parameters with nested objects, mutation operation, no annotations, no output schema), the description is completely inadequate. It doesn't explain what a 'quality rule' is in the DataWorks context, what fields can be updated, what the typical update workflow looks like, or what happens after invocation. For such a complex tool, the single-phrase description fails to provide the necessary context for an AI agent to use it 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 schema description coverage is 100%, meaning all parameters are documented in the input schema itself. The description adds no parameter information beyond what's already in the schema - it doesn't explain relationships between parameters, provide examples of common configurations, or clarify which parameters are most important. With complete schema coverage, the baseline score of 3 is appropriate since the description doesn't add value but also 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 '更新质量规则' (Update quality rule) is a tautology that restates the tool name 'UpdateDataQualityRule' without providing any meaningful elaboration. It doesn't specify what resource is being updated (DataWorks quality rules), what fields can be modified, or how this differs from sibling tools like 'CreateDataQualityRule' or 'DeleteDataQualityRule'. The purpose is stated but lacks specificity and 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 no guidance on when to use this tool versus alternatives. There are sibling tools like 'CreateDataQualityRule', 'DeleteDataQualityRule', 'GetDataQualityRule', and 'ListDataQualityRules', but the description offers no comparison or context about when updating is appropriate versus creating new rules or using read operations. No prerequisites, constraints, or typical use cases are mentioned.

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