get_table_health_metrics

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

With no annotations, the description carries full burden. It discloses that the output schema is undocumented, that columns/rows are passed verbatim, and that Data Warehouses are rejected with a ToolError. It also lists the types of issues surfaced, providing comprehensive behavioral context.

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, front-loaded with purpose and constraint, followed by details and parameter explanations. Every sentence adds value without redundancy.

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 context of no annotations, 3 parameters, and an output schema, the description covers purpose, endpoint constraint, behavioral caveats, and parameter semantics completely. It leaves no critical gaps for an AI agent to select and invoke the tool correctly.

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 input schema has 0% description coverage, but the description's Args section provides detailed descriptions for all three parameters, including constraints for 'item' and an example for 'qualified_name'. This fully compensates for the schema gap.

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 returns health metrics for a table via a specific stored procedure. It uniquely identifies the resource and action, and the sibling list contains many table-related tools, but health metrics is distinct.

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 explicitly states that it is only supported on SQL Analytics Endpoints, not Data Warehouses, providing a clear constraint. It implies usage for detecting layout issues, but does not mention alternatives or when not to use it among the many siblings.

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