dataverse_list_views

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

Annotations indicate read-only, idempotent, and non-destructive behavior. Description reinforces by stating it returns metadata and does not modify data. Adds context about return fields beyond 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?

Four sentences, each serving a clear purpose: purpose, return fields, filter guidance, and cross-reference. No redundancy, front-loaded with key information.

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?

Covers return metadata and filtering adequately. For a list tool with output schema, it is sufficiently complete. Could mention default top value or pagination, but not essential.

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?

Description adds minimal value over input schema descriptions. It lists filter options concisely, but the schema already provides equivalent detail. No additional parameter context beyond existing 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?

Description clearly states it lists saved views (savedqueries) and returns specific metadata fields. It differentiates from sibling tools like dataverse_get_view, which retrieves layout details.

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?

Explains filtering by table_logical_name and query_type with value mappings. Suggests using dataverse_get_view for layout, providing context on when to use an alternative. Lacks explicit exclusion criteria.

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