DataWorks MCP Server

GetDataQualityEvaluationTaskInstance

Retrieve detailed information about a specific data quality evaluation task instance in DataWorks to monitor and verify data integrity.

Instructions

获取数据质量校验任务实例详情

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "GetDataQualityEvaluationTaskInstance"
  }
}

Input Schema

Name	Required	Description	Default
`Id`	No	数据质量监控实例ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 18, 2025

TDQS

C2.7/5.0

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. It implies a read operation ('获取' meaning get), but doesn't specify permissions, rate limits, error conditions, or output format. For a tool with no annotation coverage, this is a significant gap in transparency.

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 sentence in Chinese that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it highly concise and well-structured.

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

Completeness2/5

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

Given the complexity of data quality tasks and the lack of annotations and output schema, the description is incomplete. It doesn't explain what details are returned, potential side effects, or how it fits into broader workflows, leaving significant gaps for a tool that likely returns structured instance information.

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 100% description coverage for its single parameter 'Id' (数据质量监控实例ID), so the schema fully documents the parameter. The description adds no additional semantic context beyond what's in the schema, resulting in a baseline score of 3 as the schema does the heavy lifting.

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 '获取数据质量校验任务实例详情' (Get data quality evaluation task instance details) clearly states the verb '获取' (get) and resource '数据质量校验任务实例详情' (data quality evaluation task instance details), providing a basic purpose. However, it doesn't distinguish this tool from similar siblings like 'GetDataQualityEvaluationTask' or 'GetTaskInstance', making it somewhat vague in comparison.

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 siblings like 'ListDataQualityEvaluationTaskInstances' (for listing) and 'GetDataQualityEvaluationTask' (for task-level details), there's no indication of context, prerequisites, or exclusions, leaving usage unclear.

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

Deploy Server