list_analytical_datasets

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

With no annotations provided, the description carries the full transparency burden. It correctly frames the operation as a read-only listing and explicitly states what is returned, which is the most important behavioral trait. It doesn't mention auth or side effects, but for a list operation this is reasonably sufficient.

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 sentences, front-loaded with the core verb and object, and every sentence adds useful information about purpose, scope, or return value. There is no wasted verbiage.

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 paginated list tool with no output schema, the description adequately covers what the tool does, its scope, and what it returns. It doesn't need to repeat schema details, and the overall context is sufficient for an agent to select it appropriately.

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 already covers all four parameters with descriptions, examples, and defaults, so the description adds no additional parameter semantics. This matches the baseline score of 3 when schema coverage is high.

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 ('List') and a specific resource ('analytical datasets within a specific asset'), clearly distinguishing this discovery tool from query-oriented or metadata-only siblings. It also names the expected return content (entity sets, names, types, URLs), making the purpose unmistakable.

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 clearly frames when to use the tool: to discover analytical datasets/models that can later be queried for business intelligence. It does not explicitly name alternative tools or exclusion criteria, but the 'can be queried' phrasing implies this is the discovery step, so the usage context is clear.

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