dataset_dimensionsList the dimensions of one cube, in path order, with each dimension's valid value codes and labels. Selections in get_data are positional and must follow this dimension order. Get the cube code from list_datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| cube | Yes | Cube code, e.g. "as1001rs" (from list_datasets). | |
| lang | No | Label language (default "en"). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "cube": "as1001rs"
+ },
+ {
+ "cube": "as1001rs",
+ "lang": "sk"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, idempotent read operation. The description adds value by specifying output includes path order, valid value codes, and labels, and that order must be followed in get_data. No contradictions.
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?
Three concise sentences, each purposeful. Front-loaded with the main action, no wasted words.
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?
For a simple tool with two well-described parameters, the description fully explains what it returns and how it connects to other tools. No output schema needed.
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?
Schema coverage is 100%, so the baseline is 3. The description adds no new parameter-level info beyond emphasizing the order constraint for get_data, which is output usage rather than parameter semantics.
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 clearly states the tool lists dimensions of one cube in path order with codes and labels, distinguishing it from siblings like list_datasets and get_data. Verb 'list' and resource 'dimensions of one cube' are explicit.
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 explains the cube code comes from list_datasets and that dimension order is crucial for get_data selections, providing clear context. It lacks explicit when-not-to-use, but the guidance is sufficient.
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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