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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds behavioral insight by enumerating the validation checks performed (required fields, types, enums, nested objects, arrays), which goes beyond the structured annotation data and gives the agent a clearer expectation of tool behavior.

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 three sentences, each earning its place. The first sentence states the core purpose, the second details specific validation coverage, and the third provides contextual importance. It is front-loaded and free of unnecessary fluff or repetition.

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 tool's moderate complexity, the description is complete: it states the purpose, the validation details, and the primary use cases. Since an output schema exists (as indicated by context signals), the description does not need to explain return values. It adequately covers the necessary context for an agent to select and invoke the 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 already provides full descriptions for both parameters (100% coverage), which sets a baseline of 3. The description adds value by explaining the validation semantics (tests required fields, types, etc.), giving the agent a better understanding of how the parameters are used in practice.

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 opens with a specific verb ('Validate') and a clearly defined resource ('LLM JSON output' against 'a JSON Schema definition'). It explicitly calls out key validation aspects (required fields, types, enums, nested objects, arrays) and differentiates itself from generic validators by focusing on LLM outputs and structured output testing.

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 provides clear context for when to use the tool: 'Critical for function-calling and structured output testing.' While it does not explicitly name alternatives or exclusion criteria, the stated use cases are specific enough to guide an agent, especially given the sibling-tool context.

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