describe_dataset

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

With no annotations provided, the description carries the behavioral burden. It discloses the return format on success and failure, the sample row cap of 5, the default of 0, and that it describes column definitions and row count. 'Show' implies a read-only operation, though it does not explicitly state that no data is modified.

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 well-structured with a front-loaded purpose sentence, followed by usage context, Args, Returns, and an Example. Every section earns its place, and the format makes key facts easy to scan.

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?

Despite having no annotations, the description is complete for a two-parameter introspection tool: it covers purpose, when to use it, parameter semantics, expected return shape, error behavior, and an example call. No critical operational detail is missing.

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 has no property descriptions (0% coverage), so the description must fully explain parameters. It does: 'Exact dataset name, e.g. "Candidates"' adds precision, and 'Optional number of first rows to include, 0-5 (default 0)' clarifies range, default, and meaning beyond the bare schema.

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 opens with a specific verb and resource: 'Show a dataset's full column definitions (types and constraints) and row count.' This clearly differentiates it from sibling tools like search_rows or get_row, which retrieve data rows rather than schema metadata.

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 states 'Use this before adding or searching rows so you know the exact column names, types, and constraints,' giving clear when-to-use guidance. It does not explicitly name alternatives or state when-not-to-use, but the intended context is unambiguous.

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