datamodel.get_elasticubes

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

Annotations already mark this as readOnly and non-destructive, and the description adds useful behavioral context: it calls the legacy v1 endpoint, works on both Linux and Windows, returns only basic metadata fields, and describes both success and error return shapes. No contradiction with annotations exists.

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 compact and front-loaded with the primary purpose, followed by endpoint details, usage guidance, and return behavior. Every sentence contributes distinct information with no redundant filler.

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 zero-parameter read-only listing tool, this description is complete: it specifies the endpoint, platform compatibility, metadata scope, alternative tool, and the return/error format. There is no output schema, so the explicit return type fills that gap.

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 tool has zero parameters and the schema is empty, so there is nothing for the description to add. The description correctly avoids inventing parameter details, matching the baseline of 4 for a no-parameter tool.

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 opens with a specific verb and resource: 'List all ElastiCubes'. It also names the exact legacy v1 endpoint and explicitly distinguishes itself from the sibling tool datamodel_get_all_datamodel by platform support and metadata richness.

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 gives explicit selection criteria: prefer get_all_datamodel for Linux deployments when richer metadata is needed, and use get_elasticubes for Windows environments or when a lightweight list suffices. This directly tells an agent when to choose this tool over the alternative.

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