get_dataset

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

With no annotations, the description carries full burden. It notes read-like behavior (retrieves metadata) and provides error handling (404, permissions). It does not mention idempotency or side effects, but the tool is clearly a read operation.

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 well-structured with clear sections (purpose, usage, parameters, returns, examples, errors). It front-loads the key action and uses bullet points for easy scanning, with no wasted sentences.

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 tool's simplicity (3 params, read operation) and presence of an output schema, the description covers all necessary aspects: purpose, parameters, expected returns, examples, and error guidance. It is complete for its complexity.

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 description coverage is 0%, but the description adds meaning for all three parameters: dataset_id (unique identifier), workspace_id (optional, omit for My workspace), and format (json or markdown defaults). This compensates well for missing schema descriptions.

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 'Get detailed information about a specific dataset' and distinguishes from siblings like get_datasets (which lists datasets) and get_refresh_history. It uses a specific verb-resource pair and contrasts with other tools.

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 lists bullet-pointed use cases (e.g., 'Check dataset configuration') and includes error handling advice referencing get_datasets as an alternative. However, it does not explicitly state when not to use this tool or directly compare with siblings like get_parameters or get_reports.

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