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DataWorks MCP Server

DataWorks MCP Server

by aliyun
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

ListDataQualityEvaluationTaskInstances

Retrieve instances of data quality evaluation tasks in DataWorks to monitor and analyze data validation results across specified time periods and projects.

Instructions

查询质量监控任务实例列表

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": "ListDataQualityEvaluationTaskInstances"
  }
}

Input Schema

NameRequiredDescriptionDefault
ProjectIdNoDataWorks工作空间的ID
PageSizeNo列表分页查询页大小，默认为10
PageNumberNo列表分页查询页码，默认为1
TableGuidNo表在数据地图中的唯一ID
TriggerClientNoTriggerContext中的TriggerClient
DataQualityEvaluationTaskIdNo数据质量校验任务ID
BizdateToNo质量校验任务业务截止日期
CreateTimeFromNo数据质量校验任务实例最早开始时间
CreateTimeToNo数据质量校验任务实例最晚开始时间
BizdateFromNo质量校验任务业务起始日期
TriggerClientIdNoTriggerContext中的TriggerClientId

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only states it's a query operation, but doesn't disclose behavioral traits like pagination behavior (implied by PageSize/PageNumber), return format, rate limits, authentication needs, or whether it's read-only. For a list tool with 11 parameters, this is insufficient.

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

Conciseness4/5

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. It's appropriately sized for a list operation, though it could be more informative without losing conciseness.

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 (11 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, filtering logic, or how parameters interact (e.g., date ranges, pagination). For a data quality monitoring tool with many filtering options, more context is needed.

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?

Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline of 3 for high schema coverage.

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 '查询质量监控任务实例列表' (Query quality monitoring task instance list) states a clear verb ('查询' - query) and resource ('质量监控任务实例列表' - quality monitoring task instance list), but it's vague about scope and doesn't distinguish from siblings like 'ListDataQualityEvaluationTasks' or 'ListTaskInstances'. It lacks specificity about what exactly is being listed.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions, leaving the agent to 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.

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