get_dataset_columns_v1_datasets__provider___dataset__columns_get

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

With no annotations, the description carries the full burden. It discloses the HTTP method (GET), public access, use of sampling for efficiency on large datasets, and view-related behavior (computed/joined columns). These are useful behavioral traits beyond the obvious.

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 concise and well-structured: it starts with the purpose, lists returns, mentions sampling, and explains view behavior in three short paragraphs. Every sentence adds value, and there is no redundancy.

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 lacking an output schema, the description lists all key return fields (column names, types, distinct counts, value distributions, row count). It also covers edge cases (view active, default_view, sampling) and is complete for its simple read operation.

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 low (33%), but the description adds meaning for the view parameter, explaining its purpose and special value 'all'. The provider and dataset parameters are implied by the tool name and path, so the description provides sufficient context for an AI agent.

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 states 'Get Dataset Columns — Get column metadata and statistics for a dataset.' It specifically lists the returned data: column names, types, distinct counts, value distributions, and row count. This clearly differentiates from sibling tools like get_dataset_column_detail, which returns details for a single column.

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 explains when to use the view parameter and the behavior with active views, including using default_view or 'all' for raw columns. It provides clear context but does not explicitly state when not to use the tool or contrast with alternatives.

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