list_columns

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

No annotations are provided, so the description carries the burden. It states the tool lists columns and data types, which implies a read-only operation, but does not explicitly declare side effects, authentication needs, or any limitations. For a simple listing tool this is acceptable, but more transparency (e.g., confirming no side effects) would be better.

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 two sentences, both concise and purposeful. The main functionality is front-loaded, and the usage tip adds value without repetition. No filler or ambiguity.

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 there is an output schema (not shown but indicated), the description need not explain return values. It covers what the tool does and when to use it. Minor absence of any note about performance or dataset-specific nuance, but overall adequate for a no-parameter, discovery-focused 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?

The tool has zero parameters, so the baseline is 4. The schema is trivially complete, and there is no parameter semantics to add. The description correctly focuses on the tool's purpose rather than 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 clearly states the action (list), resource (all columns in the customers dataset), and includes the data type output. It also implies a discovery role before column-specific queries. However, it does not explicitly differentiate from sibling tools like get_dataset_info, which might also return column list, so it lacks direct sibling distinction.

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 provides a clear when-to-use hint: 'before running specific column queries.' But it does not mention when not to use it or explicitly reference alternative tools (e.g., describe_column or get_dataset_info). The guidance is sufficient for a simple tool but lacks exclusionary guidance.

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