get_distinct_values

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

No annotations are provided, so the description carries the full burden. It discloses the return format ({value, count} ordered by count descending), automatic uppercasing of table and column, and the default limit of 20. This adequately tells the agent what to expect, though it doesn't detail authentication or side effects (which are minimal for a read operation).

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 extremely concise: two sentences covering purpose, usage, and behavior. Every sentence adds value with no 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?

Despite having no output schema, the description provides the return format and details all parameters. Combined with the clear purpose and usage guidance, the description is complete for a read-only analytical 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 coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by mentioning automatic uppercasing of table and column, which is not in the schema descriptions. It also describes the return format, which compensates for the lack of an output schema.

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 distinct values of a column with row counts, ordered by frequency, and specifies the use case of understanding value ranges before writing WHERE clauses or CHECK constraints. This distinguishes it from sibling tools like count_rows or sample_table.

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 when to use the tool ('Use this to understand the range of values before writing WHERE clauses or CHECK constraints'), providing clear context. It does not explicitly mention alternatives or when not to use, but the guidance is strong for its specific purpose.

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