count_view_rows

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

No annotations provided, so description carries full burden. It discloses the SQL operation (COUNT_BIG(*)), scope (views on two platforms), implies read-only behavior, but does not mention permissions, performance implications, or consistency guarantees.

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?

Description is concise, using bullet points and a code snippet. Every sentence adds value with no redundancy. Information is front-loaded with the purpose.

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 simplicity and presence of output schema, description covers purpose, scope, and parameters. Missing edge cases (e.g., empty view, permission errors) but these are minor for a counting tool.

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 has 0% description coverage, so description must compensate. It adds meaning by providing context for each param: workspace and item are name or GUID, qualified_name is dot-separated with an example. This helps but could be more detailed (e.g., supported formats).

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?

Description clearly states it returns total row count of a view via SELECT COUNT_BIG(*). It differentiates from sibling tools like count_table_rows (for tables) and read_view (which returns data, not just count).

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?

Description specifies it works on both Fabric Data Warehouses and SQL Analytics Endpoints, giving clear context. However, it does not explicitly mention when to use this tool vs alternatives like count_table_rows or read_view, though the purpose implies usage for row count only.

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