get_table_columns

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

No annotations are provided, so the description bears full responsibility. It correctly implies a read operation without side effects but omits details on error handling, authentication requirements, or potential performance implications. The note about data types is a good disclosure of a known limitation.

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?

Three sentences, no fluff. The first sentence states the core purpose, the second adds return details, and the third provides a critical note. All content is relevant and well-organized.

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 existence of an output schema, the description adequately covers purpose, return details, and a limitation. It does not need to explain return values. Minor omission: no mention of pagination or handling large numbers of columns, but unlikely to be an issue.

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 all 3 parameters with clear descriptions, achieving 100% coverage. The description does not add additional semantic meaning beyond the schema; it merely restates the context. Per guidelines, baseline is 3 when coverage is high.

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 ('Retrieve'), the resource ('column details for a specific table in a Fivetran connection'), and what is returned ('column names, sync configuration, and metadata'). This distinguishes it from sibling tools like list_tables or get_connection_schema.

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 provides explicit use cases ('investigating schema issues or understanding table structure') and a useful limitation note about data types not being available, suggesting an alternative (querying the destination database). However, it does not compare directly to sibling tools or state when not to use it.

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