dataverse_get_column

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

Annotations already declare readOnlyHint and idempotentHint. The description adds value by explaining the return value's role as a complete definition for updates and hinting at type-specific properties. It does not contradict annotations and provides behavioral context beyond structured fields.

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 at 70 words, structured in three focused paragraphs. It immediately states the purpose, then details return properties and usage context. Every sentence is informative with no redundancy.

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 presence of a robust input schema, annotations, and output schema, the description covers all necessary context: purpose, return content, update workflow linkage, and alternative tool for picklists. No gaps remain for an agent to use this tool correctly.

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 itself provides comprehensive descriptions for all parameters, including format hints like 'Use lowercase.' The tool description adds no additional parameter semantics beyond what the schema already covers, so a baseline score of 3 is appropriate.

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 retrieves full metadata for a single column, including type-specific properties. It differentiates from sibling tools like dataverse_list_columns and dataverse_update_column by specifying its role as a prerequisite for updates and by directing picklist queries to a separate tool.

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?

Explicit guidance: 'Use before updating a column — pass the returned object as full_definition to dataverse_update_column.' Also advises using dataverse_list_choice_column_options for picklist option values, clearly indicating when not to use this tool.

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