preview_table

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

No annotations are present, so the description must carry the behavioral burden. It conveys a read-only preview behavior and a row limit via 'first few rows', but does not disclose whether column types/headers are included or any output caps beyond the default, leaving gaps.

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 tight sentences with the primary behavior first followed by a concrete usage hint. No filler or repetition.

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 simple preview tool with an output schema, the description covers purpose and a usage scenario, but remains thin on parameter semantics and alternative tools. It is adequate but not rich enough to fully guide an agent without additional inference.

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% and the description does not name or explain the rows parameter or explicitly map table_name to a tool argument. It only tangentially implies table via 'a table', which is insufficient compensation.

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 states a specific action (show first few rows of a table) and its goal (understand structure). This clearly distinguishes it from siblings like list_tables, which lists table names, and query_data, which performs arbitrary queries.

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 explicitly instructs to use the tool after list_tables, giving a clear sequence. However, it does not mention when to prefer query_data over this tool for more complex data exploration, so it lacks explicit when-not/alternatives.

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