preview_table

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

With no annotations provided, the description carries the full burden and does well by disclosing return format (JSON with records array, count, table name), limits (default 25, max 100), and the sampling nature of the tool. However, it does not mention potential errors (e.g., invalid table/columns) or permission requirements, but for a read-only preview this is acceptable.

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 clear sections (Description, Args, Returns, Examples) and includes practical examples. It is a bit longer than the minimal two-sentence ideal, but every section earns its place and aids comprehension.

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's moderate complexity and the presence of an output schema, the description is quite complete: it documents all parameters, return format, and provides usage examples. It does not explicitly address edge cases or the relationship to sibling tools like 'get_table_sample_data', which prevents a perfect score.

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%, yet the description compensates fully with an 'Args:' section that explains each parameter in plain language, including defaults, max values, and examples. This adds significant meaning beyond the bare schema definitions.

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 that the tool previews records from a table with optional column selection and filtering, using a specific verb and resource. However, it does not distinguish itself from the sibling tool 'get_table_sample_data', which likely performs a similar function, so sibling differentiation is lacking.

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 explicitly advises to use the DerivaML Python API for bulk data access, positioning this tool as appropriate for lightweight preview/sampling. It also implies usage for understanding data structure and content, giving clear context on when to use this tool versus the API.

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