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aliyun
by aliyun

GetDataQualityEvaluationTask

Retrieve detailed data quality monitoring results to assess and verify dataset integrity within Alibaba Cloud DataWorks.

Instructions

查询数据质量监控详情

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
IdNo数据质量监控ID
Behavior2/5

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 implies a read operation ('查询' - query), but it doesn't specify whether this requires permissions, what the return format looks like (e.g., JSON structure), or any rate limits. For a tool with no annotation coverage, this leaves significant behavioral gaps, though it correctly indicates a query rather than a mutation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is a single, efficient phrase ('查询数据质量监控详情') that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with zero waste, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no nested objects, no output schema), the description is minimally adequate. It clarifies the tool queries details rather than lists or creates, but lacks output information and behavioral context. With no annotations and no output schema, the description should ideally provide more guidance on return values or usage, but it meets a basic threshold for this simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 parameter ('Id') with 100% description coverage ('数据质量监控ID' - data quality monitoring ID). The description adds no additional parameter semantics beyond what the schema provides. With high schema coverage, the baseline is 3, as the schema adequately documents the parameter without needing extra details from the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description '查询数据质量监控详情' (Query data quality monitoring details) states a clear verb ('查询' - query) and resource ('数据质量监控详情' - data quality monitoring details), providing a basic purpose. However, it doesn't distinguish this tool from sibling tools like 'GetDataQualityEvaluationTaskInstance' or 'ListDataQualityEvaluationTasks', which appear to handle similar data quality monitoring entities. The purpose is clear but lacks sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. With sibling tools like 'GetDataQualityEvaluationTaskInstance' (likely for instances) and 'ListDataQualityEvaluationTasks' (likely for listing multiple), there's no indication of context, prerequisites, or exclusions. The agent must infer usage from the name and schema alone.

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

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