Retrieve detailed information about a specific data source in Alibaba Cloud DataWorks using its unique identifier.
查看数据源详情
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": "GetDataSource"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| Id | No | 数据源ID，数据源的唯一标识符 |
Changes observed during successful MCP inspections.
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 only states the action ('view details') without mentioning permissions required, rate limits, response format, or whether it's a read-only operation. This is inadequate for a tool with no annotation coverage, as it lacks critical behavioral context.
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 phrase ('查看数据源详情'), which is highly concise and front-loaded with the core action. There's no wasted text, but it could be slightly more informative without losing efficiency, such as by adding a brief context or usage hint.
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 complexity of data source management and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'details' include, potential errors, or how it fits into the broader context of sibling tools, leaving gaps in understanding for effective tool invocation.
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 input schema has 100% description coverage, with the parameter 'Id' documented as '数据源ID，数据源的唯一标识符' (data source ID, the unique identifier of the data source). The description adds no additional parameter information beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
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 '查看数据源详情' (view data source details) clearly states the verb ('view') and resource ('data source details'), providing a basic understanding of the tool's purpose. However, it doesn't differentiate from sibling tools like 'ListDataSources' or 'GetDataServiceApi', which also retrieve data source information, 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It doesn't specify prerequisites (e.g., needing a data source ID), exclusions, or comparisons to siblings like 'ListDataSources' for listing all data sources or 'GetDataServiceApi' for API-related details, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.