Fuzzy search column values

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

No annotations are provided, so the description carries full burden. It discloses the key non-obvious behavior: it samples up to sampleSize distinct values server-side and ranks them client-side. However, the wording ambiguously mentions 'rows' versus 'distinct values', and doesn't specify return format.

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?

Two sentences, front-loaded with purpose, then method. No filler or redundant information.

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?

No output schema exists, so the description should explain return values. It explains the algorithm but not what fields are returned or whether results are distinct values or actual rows. This is a notable gap for an agent deciding whether to use this 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 descriptions cover all 6 parameters 100%. The description adds context around sampleSize (sampling server-side, client-side ranking) which explains its effect on result quality. It doesn't add meaning for other parameters, but the schema already explains them well.

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 uses a specific verb ('fuzzy search'), names the resource ('distinct values of a single text column'), and clarifies the goal ('find rows whose value is close...'). It clearly differentiates from exact-match search, making its purpose unambiguous.

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?

It explicitly says 'use this to find rows whose value is close to (but not an exact match for) a search term', providing a clear when-to-use context. It doesn't mention alternatives or exclusions, but the guidance is sufficient for most cases.

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