dataset_get

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

The description explicitly marks the tool as a read operation with '[READ]', which is the primary behavioral trait. Without annotations, this is valuable transparency. It does not disclose error handling or authentication needs, but for a simple read, the key information is provided.

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 extremely concise: a one-line summary with a read flag and example, then brief parameter descriptions. Every sentence adds value, and the structure makes it easy to scan. 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?

The description adequately covers the parameters but does not describe the return value or behavior on failure (e.g., if dataset not found). For a simple get with no output schema, the description is moderately complete but could be improved by mentioning the response format.

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?

Despite 0% schema coverage, the description explains both parameters: dataset_id is a TrueNAS dataset id from dataset_list, and target is a TrueNAS target name from config. This adds meaning beyond the bare schema (type/required/default), helping the agent understand their origins.

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 returns detail for a single dataset by id, with a specific verb 'Read' and resource 'dataset detail'. The example 'tank/data' adds clarity. It distinguishes from sibling tools like dataset_list (list) and dataset_create (create).

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 for retrieving a single dataset's details when you have its ID. It references dataset_list for getting the ID, providing context. However, it does not explicitly state when not to use or specify alternatives, though the sibling names make the differentiation clear.

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