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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds that it returns a test plan with a scored rubric, which is useful but not extensive behavioral disclosure. No contradiction.

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?

Two sentences, front-loaded with purpose and action, no wasted words. It efficiently states what the tool does and returns.

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?

The description orients the agent on the tool's role (test suite definition) and return value (test plan with rubric). Given the output schema and 100% parameter schema coverage, the description is sufficient for selection and invocation despite numerous parameters.

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 description coverage is 100%, so the baseline is 3. The description references 'system prompt, user prompt, and expected output criteria,' loosely mapping to several parameters but adds no deeper meaning beyond what the schema already provides.

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 uses a specific verb and resource ('Define a test suite for a prompt') and clearly distinguishes from siblings like run_semantic_tests by stating it produces an input for evaluation, not the evaluation itself.

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 phrase 'use this as input for manual or automated LLM evaluation' provides clear context for when to use the tool. It does not explicitly name alternatives or exclude other tools, but the intended workflow is evident.

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