dataset_list

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

No annotations are provided, so the description carries the full burden. It does disclose a key behavior (includes both linked and unlinked datasets), but does not mention pagination, result fields, or any other side effects. For a simple list tool, this is minimal but acceptable.

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 a single, concise sentence that immediately conveys the tool's purpose. There is zero wasted information and the phrasing is front-loaded.

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 tool is simple with one optional param and no output schema. The description covers the core functionality (listing all datasets, including linked/unlinked) but lacks detail about the response format or fields. Given the low complexity, this is nearly complete, but a bit more context about return values would be helpful.

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 schema has only one parameter (data_type) with a clear description and enum, so schema coverage is 100%. The tool description adds no additional parameter semantics, keeping the baseline at 3.

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 verb 'List' and the resource 'datasets', with scope 'all datasets (linked and unlinked)'. This distinguishes it from the sibling tool dataset_get, which targets a specific dataset, and from project_list which lists projects.

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 (when you need all datasets), but does not explicitly state when to use this over dataset_get. It also does not mention any exclusions or alternative tools. The 'all' wording hints at broad coverage but lacks direct guidance.

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