ms-fabric-mcp-server

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

No annotations are provided, so the description carries full burden. It mentions executing a query and returning a raw response, but lacks critical behavioral details: whether this is read-only or mutating, authentication requirements, rate limits, error handling, or what 'raw Power BI response' entails (e.g., format, size limits). This is inadequate for a tool with potential data access implications.

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, efficient sentence that front-loads the core action and outcome with zero wasted words. It's appropriately sized for a straightforward tool, though its brevity contributes to gaps in other dimensions.

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 4 parameters with 0% schema coverage, no annotations, and an output schema (which mitigates need to describe return values), the description is incomplete. It covers the basic purpose but misses parameter semantics, behavioral context, and usage guidelines. It's minimally viable but has clear gaps for a tool that interacts with data systems.

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 description coverage is 0%, so the description must compensate. It adds no information about parameters beyond what the schema names imply. For example, it doesn't explain what 'workspace_name' refers to, the format of the 'query' (DAX syntax), or when to use 'semantic_model_name' vs 'semantic_model_id'. This leaves key usage details undocumented.

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 ('Execute a DAX query') and the outcome ('return the raw Power BI response'), which is specific and unambiguous. It distinguishes this as a query execution tool rather than data manipulation or management, though it doesn't explicitly differentiate from potential query-related siblings (none are listed).

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a workspace or semantic model), exclusions, or comparisons to other tools like 'get_semantic_model_definition' for metadata queries. Usage is implied only by the tool name and parameters.

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