ntd_list_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 of behavioral disclosure. It states the tool returns 'all distinct values' but does not specify the output format (e.g., array of strings, sorted or unsorted), limits, pagination, or what happens if the column does not exist. It also fails to warn about potentially large result sets for high‑cardinality columns.

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 consists of two short, front‑loaded sentences with no wasted words. The core action ('All distinct values') is stated first, followed by the use case. It is optimally concise for a simple tool.

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?

For a tool with one parameter and no output schema, the description covers the main purpose but lacks details about the return value structure (e.g., array of unique values), error conditions, and limitations. Given the simplicity, it is adequate but not thorough.

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 covers 100% of the single parameter 'field' with a clear description and examples. The tool description adds the context that the column is for 'any column' and ties to filter discovery, but it does not add new semantic details beyond the schema. 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 that the tool retrieves 'All distinct values for any column' and frames it as useful for 'discovering valid filter values'. This indicates a specific verb (list) and resource (distinct values of a column). However, it does not explicitly differentiate from siblings like ntd_schema or ntd_summary_stats, which might also provide column-level information.

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 phrase 'Useful for discovering valid filter values' implies a common use case—exploring column values before applying filters. But there is no mention of when not to use it (e.g., high‑cardinality columns, performance concerns) or how it compares to siblings like ntd_schema (which might list columns but not values). The guidance is implied but not explicit.

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