list_datasets

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

With no annotations provided, the description carries the full burden and succeeds by detailing the specific output fields returned (id, name, configured-by, web URL, is-refreshable, storage mode). It also clarifies the 'semantic models' synonym, adding helpful domain context.

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 tightly focused sentences: purpose declaration, output specification, and chaining instruction. Every sentence earns its place. Information is front-loaded with the core action in the first sentence.

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 has an output schema (per context signals), the description adequately summarizes return values without needing full duplication. The inclusion of semantic model terminology and field-level output details provides good domain coverage. Only gap is the undocumented required parameter.

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 coverage is 0% (only title 'Workspace Id' provided). The description mentions 'in a Power BI workspace' which provides semantic context for the parameter, but fails to explicitly name `workspace_id` or explain how to obtain it (e.g., via `list_workspaces`). Insufficient compensation for the schema gap.

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 action (List), resource (datasets/semantic models), and scope (Power BI workspace). It effectively distinguishes from sibling tools like `get_dataset_info` (list vs. get) and `list_workspaces` (datasets vs. workspaces) through specific terminology.

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?

Provides clear guidance on output chaining ('Use the `id` field as `dataset_id` in subsequent tools'), which helps the agent understand the tool's role in workflows. Lacks explicit contrast with `get_dataset_info` for when to use list vs. get, but implies bulk retrieval through 'List all'.

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