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

With no annotations, the description carries the full burden and does well: it reveals the two execution paths (enum return vs capped SELECT DISTINCT), explains `truncated` as a cap flag, and notes case-insensitive prefix matching. It does not mention whether the operation is read-only or any error/rate-limit behavior, but 'List' implies a safe read and the behavioral details add meaningful value beyond the schema.

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 dense and front-loaded with the purpose and key usage context, followed by parameter behavior and execution details. Every sentence earns its place, though it could be slightly more structured for scanning. Overall it is well-organized and efficient.

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 the primary workflow of discovering valid filter values before querying, the description is complete: it states prerequisites, parameter constraints, and behavior. The output schema exists, so return values need no explanation. However, the roles of `main_topic` and `detail` remain under-specified, so a fully autonomous agent might still need extra inference for those optional 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?

Schema description coverage is 0%, so the description must compensate. It clarifies `column` (must be filterable), `prefix` (case-insensitive starts-with), and `limit` (caps results, default 50). However, optional parameters like `topic`, `main_topic`, and `detail` are not explained, leaving gaps for a full understanding of all six parameters.

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 opens with a precise verb+resource statement: 'List the distinct values of ONE filterable column of a topic.' It then differentiates itself from related siblings like query_dataset and describe_dataset by framing itself as 'the fast way to learn valid filter values before query_dataset.' No ambiguity about what the tool does.

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?

Explicitly tells the agent when to use it: before query_dataset, especially for free-form categoricals and FK codes lacking enums in describe_dataset. It warns that guessing wrongly yields an empty page with no error, and points the agent to describe_dataset's `filters` for column eligibility. This gives strong when/why context and names alternatives.

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