ms-fabric-mcp-server

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

With no annotations provided, the description carries the full burden. It discloses behavioral traits such as returning all items by default, supporting optional filtering, listing supported types, and detailing the return structure. It does not mention rate limits, authentication needs, or pagination, but covers core behavior adequately for a read operation.

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 well-structured with clear sections (purpose, parameters, returns, example) and uses bullet points for types. It is appropriately sized but includes some redundancy (e.g., repeating 'Returns all items' after the opening sentence). Every sentence adds value, though minor trimming could improve efficiency.

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's low complexity (read-only list operation), no annotations, 0% schema coverage, but with an output schema implied in the description, the description is complete. It covers purpose, parameters, return values, and examples, providing all necessary context for an agent to invoke the tool correctly without gaps.

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 description coverage is 0%, so the description must compensate fully. It clearly explains both parameters: workspace_name as 'the display name of the workspace' and item_type as an optional filter with examples and default behavior. This adds significant meaning beyond the bare schema, fully documenting parameter usage.

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 verb ('List') and resource ('all items in a Fabric workspace'), specifies optional filtering by type, and distinguishes from siblings like list_workspaces (which lists workspaces, not items within them). It provides specific examples of item types, making the purpose unambiguous.

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 implies usage context by stating it returns items 'in the specified workspace' and mentions filtering by item_type. However, it does not explicitly state when to use this tool versus alternatives like list_notebook_executions or get_semantic_model_details, which might overlap for specific item types. The guidance is clear but lacks explicit sibling differentiation.

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