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List semantic models

list_semantic_models

List semantic models in a Power BI workspace. Specify workspace ID or omit for My workspace.

Instructions

List semantic models in My workspace or in a specific workspace by id. Use list_workspaces first to resolve workspace ids. If workspace discovery is not authenticated and the user did not name a workspace, ask the user which workspace to use.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceIdNoWorkspace/group id. Omit for My workspace.
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It discloses the two workspace contexts but lacks information on side effects, permissions, error handling, or rate limits. For a listing tool, basic operational traits are missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose in the first sentence. No wasted words; every sentence adds value. Ideal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should clarify the return format or structure, which it does not. It also fails to address error cases (e.g., invalid workspaceId). While the tool's name implies the output, the description is minimally adequate for a simple listing tool, but lacks completeness for edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% coverage with the workspaceId parameter described. The description adds value by explaining when to omit workspaceId (for My workspace) and how to handle workspace discovery. This extra context goes beyond the schema, earning a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists semantic models and specifies two scopes (My workspace or specific workspace). It is specific with verb+resource, but does not explicitly differentiate between the sibling tool list_semantic_models_in_workspace_via_modeling_mcp, so it loses points for sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises to use list_workspaces first to resolve IDs and provides guidance for handling unauthenticated workspace discovery. However, it does not compare with the sibling list_semantic_models_in_workspace_via_modeling_mcp, nor does it specify when to use this tool versus alternatives, leaving ambiguity.

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

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