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 of behavioral disclosure. It implies a write operation ('Add') but does not specify permissions required, whether the addition is reversible, potential side effects on the semantic model, or any rate limits. This leaves significant gaps in understanding the tool's behavior and safety.

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, direct sentence that efficiently conveys the core action without unnecessary words. It is front-loaded with the main purpose, making it easy to grasp quickly, and every part of the sentence serves to clarify the tool's function.

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 complexity (5 required parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is minimally adequate. It states what the tool does but lacks details on behavior, parameter meanings, and usage context, which are crucial for a mutation tool with multiple inputs. The output schema helps, but the description itself is incomplete.

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 schema provides no parameter descriptions. The description mentions 'table from a lakehouse' and 'existing semantic model,' hinting at some parameters, but it does not explain the purpose of 'workspace_name,' 'columns,' or the structure of 'SemanticModelColumn.' This fails to compensate for the lack of schema documentation, leaving key parameters unclear.

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 ('Add a table') and the target resources ('from a lakehouse to an existing semantic model'), which is specific and understandable. However, it does not explicitly differentiate from sibling tools like 'add_measures_to_semantic_model' or 'add_relationship_to_semantic_model', which handle different aspects of semantic models, so it misses full sibling differentiation.

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, such as 'create_semantic_model' for initial setup or other 'add_' tools for different model components. It lacks context on prerequisites, exclusions, or typical scenarios, offering only a basic statement of function without usage direction.

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