base_tableUsage

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

The description discloses key behavior: it can materialize results as a volatile table when 'persist' is True. This goes beyond the schema. However, it does not explicitly state whether the operation is read-only (likely safe) or if any side effects occur, though the context suggests it is non-destructive.

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 concise with one clear sentence and a bullet-like list of arguments. It is front-loaded with purpose. Minor improvement could be better structuring, but it remains efficient and readable.

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 tool has no output schema and no annotations, so the description must fully explain behavior and output. It explains the 'persist' behavior but does not describe the output format (e.g., columns returned) for the non-persist case. This leaves a significant gap for agents needing to interpret the result.

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 description coverage is 100%, so the schema already documents both parameters. The description adds no additional meaning beyond repeating the schema's parameter descriptions. Baseline 3 is appropriate.

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 (measure usage) and the resource (tables and views in a schema), with a specific goal (infer active objects). It distinguishes itself from sibling tools like base_tableList or base_tableDDL by focusing on usage measurement.

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 implies the tool is for measuring usage but does not explicitly state when to use it versus alternatives (e.g., other table analysis tools). No 'when not to use' or alternative suggestions are provided, which is a gap given the large number of sibling tools.

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