dataverse_get_audit_details

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, indicating a safe read operation. The description adds value by explicitly stating the auditing requirement and the polymorphic return types, which are important behavioral details not covered by annotations.

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 (5 sentences), well-structured, and front-loaded with the main purpose. It lists common subtypes in a clear, scannable format without unnecessary words.

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 annotations and the presence of an output schema, the description covers all necessary context: purpose, return types (polymorphic subtypes), prerequisites (auditing enabled), and parameter origin. No gaps remain.

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 provides descriptions for both parameters (audit_id and dataverse_url). The description further clarifies that audit_id comes from listing or change history tools, adding helpful context beyond the schema alone.

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 'Retrieve full details from a single audit record' and explains the function call, subtypes, and scope. It distinguishes from sibling tools like dataverse_list_audit and dataverse_retrieve_record_change_history by focusing on full detail retrieval for a single audit record.

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 notes the prerequisite of auditing being enabled and the required audit_id format. It implicitly indicates when to use (when full details of a specific audit record are needed), but does not explicitly list when not to use or compare to siblings.

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