get_dataset_sample

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

No annotations provided, so description bears full load. It fails to mention that this is a read operation, no side effects, or any permissions needed. Minimal behavioral info.

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?

Short single sentence, but too sparse for a tool with 4 parameters. Could include key details without being verbose.

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?

No output schema; description should mention return format (e.g., array of rows). Lacks context on sample behavior or limits. Incomplete for a data retrieval 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?

Schema coverage is 100%, so baseline 3. Description adds no extra parameter meaning beyond what schema already provides. Neutral.

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?

Description states 'Get sample data from datasets' - verb+resource is clear. Distinguishable from siblings like get_dataset_info or inspect_dataset_schema, but could specify sample type (e.g., random).

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?

No guidance on when to use vs. other dataset tools. With many siblings, explicit context for when to sample vs. get full info is missing.

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