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. It discloses key behavioral traits: the job runs asynchronously, returns a dictionary with specific fields, and provides example usage. However, it doesn't mention authentication requirements, rate limits, error conditions, or what happens if the job fails. The description doesn't contradict any annotations since none exist.

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 purpose statement, common job types, behavioral note, parameter explanations, return value description, and example. Every section adds value. It could be slightly more concise by integrating the example more tightly, but overall it's appropriately sized and front-loaded with 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 5 parameters with 0% schema coverage, no annotations, but with output schema (implied by 'Returns' section), the description does well. It covers purpose, usage, parameters, returns, and provides an example. The main gap is lack of error handling or permission information. The output schema existence reduces the need to fully document return values, but the description still provides useful context about what fields mean.

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?

With 0% schema description coverage for 5 parameters, the description must compensate. It provides meaningful explanations for all parameters: workspace_name as 'display name', item_name as 'Name of the item', item_type with examples, job_type with common types, and execution_data as 'Optional execution data payload'. This adds substantial value beyond the bare schema, though it doesn't specify format constraints or validation rules.

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 tool's purpose: 'Run an on-demand job for a Fabric item' with the verb 'executes' and resource 'job for the specified item'. It distinguishes from siblings by focusing on job execution rather than creation, deletion, or status checking. However, it doesn't explicitly differentiate from all 30+ siblings, just the two status-checking tools mentioned later.

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 clear context about when to use this tool: for running jobs asynchronously. It explicitly mentions alternatives for checking progress: 'Use get_job_status or get_job_status_by_url to check the job's progress and result.' It doesn't specify when NOT to use it or compare with other job-related siblings like livy_run_statement.

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