add_set

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what gets created and why, but says nothing about permissions required, whether an existing set is overwritten, failure modes, or whether the change is saved immediately. For a mutation tool with nine parameters and no annotations, this is a significant gap.

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 front-loaded with the core action and then immediately gives the two key usage modes. It is compact and avoids filler, though the parenthetical example and second paragraph could be slightly tighter. Every sentence earns its place, but it is not maximally crisp.

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?

An output schema exists, so return values need not be explained. However, with nine input parameters at 0% schema coverage and no annotations, the description only covers two high-level usage patterns and leaves most parameter behavior and mutation side effects undocumented. It is not complete enough for an agent to invoke the tool correctly across its full parameter space.

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%, so the description must compensate for all nine parameters. It only explains the combined use of basis_field and top_n and mentions omitting them, while leaving set_name, dimension_field, members, direction, aggregation, use_all, and internal_name entirely unexplained. The added meaning is helpful but covers only a small fraction of the 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 states a specific verb and resource: 'Create a Tableau set (datasource filter-group) for membership logic.' It adds clarifying scope ('datasource filter-group') that helps distinguish it from generic grouping tools. However, it does not explicitly compare itself to close siblings like add_group or add_combined_set, so an agent must infer the distinction.

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 gives clear conditional usage: 'Use basis_field + top_n to build a top-N set' and 'omit both to create an empty set used as a Set Action target.' This tells the agent how to configure the tool for two concrete scenarios. It does not mention when to prefer this tool over siblings such as add_group or add_combined_set, which prevents a top score.

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