list_unique_values

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

The description discloses key behavioral traits: values are sorted, unique, non-null, and limited to 200. No annotations are provided, so the description carries the full burden. It does not mention read-only status or performance implications, but the stated traits are adequate for basic understanding.

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 with no wasted words. The first sentence front-loads the core functionality, and the second provides the primary use case. Every sentence adds value.

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 tool has 4 parameters and an output schema (not shown), the description covers the purpose and use case but lacks parameter documentation. It does not explain how to specify the dataset (via dataset_uri or filename) or the role of the limit parameter. While the output schema likely describes return values, the description could be more complete for a tool with multiple undocumented parameters.

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 schema has 0% parameter description coverage. The description only implicitly explains the 'column' parameter by stating 'in a column'. The 'limit', 'filename', and 'dataset_uri' parameters are not described. Since coverage is very low, the description should compensate but does not provide meaningful details about these parameters beyond their names.

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 it returns sorted unique non-null values from a column, with a maximum of 200. This distinguishes it from sibling tools like 'list_datasets' (lists datasets) or 'describe_dataset' (describes a dataset). The verb is implied (list), and the resource is column values, making the purpose specific and clear.

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 says to use this tool to discover valid filter values before passing them to query tools. This provides clear context for when to use it, but it does not explicitly mention when not to use it or name alternative tools. However, the sibling list includes query tools, so the guidance is mostly sufficient.

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