sample_table

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

The description calls the result a 'random sample' but does not disclose the sampling method, performance characteristics, or guarantee of randomness. With no annotations provided, the description carries the full burden but fails to mention that the operation is read-only or non-destructive.

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 a single, concise sentence that front-loads the core functionality. Every word serves a purpose with no redundancy or filler.

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 tool has 5 parameters and no output schema, the description should cover the return format (e.g., JSON results) or any side effects. The current description is too sparse to fully enable correct agent decision-making without additional documentation.

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?

All parameters are described in the input schema with clear names and defaults. The description adds no extra semantic value beyond the schema itself, such as explaining the effect of 'count' or how 'server' selection works.

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 ('Get a random sample of rows from a table') and the resource ('table'), making the tool's purpose immediately understandable. It also hints at a use case ('useful for understanding data patterns'), which further clarifies intent.

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 implies usage for exploratory data analysis but offers no explicit guidance on when to use this tool versus alternatives like 'execute_query' or 'describe_table'. No 'when-not-to-use' or comparisons are provided.

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