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

Goes beyond the readOnlyHint annotation by explicitly stating it is a cheap database read with no backend call, and it documents the return shape (workspace_id, datasets array, count). It doesn't mention failure modes or pagination, but the core behavioral contract is disclosed.

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 concise and information-dense: ordering, scope, use case, example, and return shape are all covered in a few sentences, with the core meaning stated first.

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?

Covers what is returned, the ordering, scope, and gives a concrete request example. It does not mention error cases or pagination beyond the limit example, but for a list endpoint with a simple schema this is reasonably complete.

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 provides no description (0% coverage), and the description includes a usage example with 'limit': 25. However, it never explicitly states that limit controls the number of datasets returned; the agent must infer this from the parameter name and min/max bounds.

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 uses a specific verb and resource: 'List ALL datasets in this workspace', explicitly scopes to owned datasets, distinguishes from the public catalog, and clarifies it returns datasets created by the user or colleagues. This clearly separates it from siblings like get_my_dataset or search_datasets.

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?

States a clear usage scenario ('Use it to find the dataset_id for a dataset you or a colleague created earlier') and an exclusion ('not the public catalog'). It does not name an alternative tool explicitly (e.g., search_datasets for public datasets), so it falls just short of full guidance.

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