sumo_qa_validate_test_data

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that no downstream mutation occurs and describes the return payload (confidence, freshness, reason), enhancing behavioral understanding beyond the 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 extremely concise—two sentences plus a bulleted list of example phrasings. Every sentence adds value, with the main verb and scope stated first. No redundant details.

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 simple validation tool with no output schema, the description covers purpose, parameter options, return contents, and usage context. It is fully sufficient for an agent to select and invoke 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?

Schema coverage is 0%, but the description explains the two parameters: entry_id (looked up in catalogue) and entry (full record dict). This adds critical meaning that the schema alone lacks, effectively compensating for missing schema descriptions.

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 validates test data entries without side effects, distinguishing it from mutation tools. It provides specific verb+resource ('Validate a test data entry') and includes common natural-language queries, making the purpose unmistakable.

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 explicitly notes the tool does not provision or mutate, indicating appropriate use for read-only validation. While it lacks explicit comparison to sibling tools, the natural-language phrasings guide usage context. No false exclusions present.

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