sample_table

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

Describes the basic behavior (return sample rows) and return format. Since no annotations exist, the description provides adequate behavioral info, but it could mention that the sample may not be random or ordered.

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?

Two concise sentences: first states purpose, second adds usage guidance and return format. No wasted words, front-loaded.

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 tool with no output schema and 3 params thoroughly described in schema, the description covers purpose, usage, and return format adequately. Minor omission: could mention that row order is unspecified.

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 100%, so the baseline is 3. The description does not add extra parameter-level details beyond the schema, but it's acceptable.

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 tool returns a small sample of rows from a table without writing SQL, distinguishing it from SQL-writing tools like run_query. It specifies the return format as a list of dicts, which is precise.

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 advises using this tool to understand real data before writing queries or DML, giving clear when-to-use guidance. Does not explicitly state when not to use, but it's implied for exploration.

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