get_entity_schema

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains that choice-style columns include an option_set summary with is_global and option_count, and it clarifies that actual option values are not returned here, pointing to get_picklist_options. This goes beyond the basic 'get schema' statement and helps the agent set expectations.

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 front-loaded with the core purpose. Each sentence adds useful information: the main action, the choice-column behavior, and the follow-up tool for option values. There is no wasted wording.

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?

For a single-parameter, no-output-schema tool, the description is reasonably complete. It covers what is returned at a high level, highlights notable behavior for choice columns, and points to the relevant sibling tool for further detail. It does not describe error cases or formatting, but those are not critical for this simple lookup tool.

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 already fully documents the single parameter with a clear description and example, so schema coverage is 100%. The description does not add much parameter-specific detail, but none is needed because the schema covers it.

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 the tool gets attributes (columns) of a specific Dataverse table, using a specific verb and resource. It also distinguishes the tool from list_entities by focusing on a single entity's schema rather than listing entities.

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 gives useful context on when to use this tool, such as when you need attributes and want to see which choice lists are shared org-wide. It explicitly directs the agent to use get_picklist_options for actual option values, which is a clear alternative. It does not explicitly mention when not to use this tool versus list_entities or get_attribute_dependencies, but the context is strong enough.

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