inspect_model

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

Without annotations, the description fully carries behavioral disclosure. It explicitly states the tool is read-only ('never modifies the model'), explains authentication and row-level security implications, and mentions the summarisation behavior. No contradictions with annotations 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 three sentences, front-loaded with the core purpose, followed by parameter modes and safety/usage notes. Every sentence adds value, with no redundancy or filler.

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?

The description covers purpose, usage modes, safety (read-only), authentication effects, and sequencing. The output schema exists, so return values do not need elaboration. For a tool with 3 optional parameters and two usage modes, this is complete.

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 coverage is 0%, but the description compensates fully by explaining the two modes: workspace+model (by display name or GUID) or folder path. This directly maps to the three parameters (workspace, model, folder) and gives meaningful context for each.

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 connects to a Power BI semantic model and summarises its contents. It distinguishes itself from siblings by explicitly noting it runs before `show_measure`, which helps differentiate it from related operations.

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?

Provides explicit usage context: 'Run this before `show_measure`' indicates the appropriate timing. It also explains the two possible input modes (Fabric workspace+model or local folder), giving clear guidance on when and how to use the tool.

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