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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds valuable behavioral context: no auth required and the data source (data.statistics.sk). It does not contradict annotations and gives a clear picture of what the tool returns (dimensions with codes and labels). Not a 5 because it omits any discussion of pagination or limits, but these are likely in the output schema.

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?

Two sentences, front-loaded with the core action, no fluff. Every sentence earns its place: the first defines what it does and its output, the second adds source and auth context. Highly efficient.

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 presence of an output schema (which would document return structure), the description fully covers input semantics, the relationship to sibling tools, and operational details (data source, auth). Nothing an agent needs to call this correctly is missing.

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% (cube_code described), and the description adds meaning beyond the schema by explaining that the cube_code comes from dataset_search and providing an example ('as1001rs'). This helps an agent understand where to obtain the value and what it represents, which goes beyond the bare schema description.

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 states a specific verb ('Get') and resource ('dimensions and valid value codes for one DATAcube table'), and clearly distinguishes it from siblings by referencing the source of its input (dataset_search) and the consumer of its output (dataset_data). It is immediately clear what this tool does and how it fits in the workflow.

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 implies usage context by stating the cube_code comes from dataset_search and that the result is needed to build the selections object for dataset_data. It doesn't explicitly name alternative tools or say 'when not to use', but the workflow is clear enough for an agent to select it appropriately. Lacks explicit exclusion guidance.

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