datamodel.get_datasecurity_detail

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

The description goes well beyond the readOnly/destructive annotations by explaining that each row is a column-level rule repeated per share, and by documenting the special interpretation of members and exclusionary values. This gives the agent essential behavioral context that annotations alone do not provide.

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: a clear purpose sentence, a row-semantics explanation, a bulleted list for special member-value handling, and a return-format note. Each part earns its place and the most critical information is front-loaded.

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?

The description covers the key behavioral nuances and return shape well, especially given there is no output schema. However, the union return type 'list[dict[str, Any]] | dict[str, Any]' is not fully clarified, and the trailing '…' leaves the complete set of returned fields slightly vague.

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?

There is only one parameter, datamodel_name, and the input schema already describes it fully at 100% coverage. The description mentions 'data model' again but adds no new semantic details about the parameter, so the baseline score of 3 applies.

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 object: 'Retrieve detailed datasecurity rules for a data model', and adds distinguishing details like 'share-level visibility' and 'column-level rule'. This clearly separates it from sibling tools such as datamodel_get_datamodel_shares or datamodel_get_table_schema.

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 establishes a clear context: use this when you need detailed datasecurity rules and their share-resolved interpretation for a data model. It does not explicitly mention alternatives or when-not-to-use conditions, so it stops short of a perfect score.

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