list_workspaces

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

No annotations provided, so description carries full burden. It discloses return values ('workspace id, name, type, and capacity information'), which is helpful. However, it omits other behavioral traits like rate limits, caching behavior, or explicit confirmation that this is read-only (though implied by 'List').

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 sentences, each earning its place: purpose statement, return value disclosure, and usage guidance. Front-loaded with the core action. No redundant or wasteful text.

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 0 parameters and existence of output schema (per context signals), the description is complete. It covers the tool's function, output summary, and integration pattern with the tool ecosystem.

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?

Input schema has 0 parameters. Per scoring rules, 0 params = baseline 4. No parameter documentation needed.

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?

Description uses specific verb 'List' with clear resource 'Power BI workspaces (groups)' and scope 'authenticated user is a member of'. Clearly distinguishes from sibling tools like list_datasets or list_tables by specifying 'workspaces'.

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 explicit guidance on using output: 'Use the `id` field as `workspace_id` in subsequent tools.' This helps the agent understand the chaining pattern, though it doesn't explicitly state when NOT to use it or name specific alternative tools.

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