ms-fabric-mcp-server

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is an 'Add' operation (implying mutation) but doesn't mention permissions needed, whether changes are reversible, rate limits, or what happens if measures already exist. For a mutation tool with 5 parameters and no annotation coverage, this is a significant gap in transparency.

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 gets straight to the point with zero wasted words. It's appropriately sized for the tool's complexity and front-loads the essential information.

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 this is a mutation tool with 5 parameters (3 required), 0% schema description coverage, no annotations, but with an output schema, the description is inadequate. While the output schema may help with return values, the description fails to explain parameter meanings, usage context, or behavioral implications, leaving critical gaps for the agent.

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%, meaning none of the 5 parameters have descriptions in the schema. The tool description provides no additional information about what 'workspace_name', 'table_name', 'measures', 'semantic_model_name', or 'semantic_model_id' mean or how they should be used. This leaves all parameters completely 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 ('Add measures') and target ('to a table in an existing semantic model'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'add_relationship_to_semantic_model' or 'add_table_to_semantic_model', which would require a 5.

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 like 'create_semantic_model' or 'delete_measures_from_semantic_model'. It mentions 'existing semantic model' but doesn't clarify prerequisites or exclusions, leaving the agent with minimal context for decision-making.

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