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

Annotations already provide readOnlyHint and idempotentHint, so the safety profile is clear. The description adds behavioral details: it detects types, required fields, array shapes, nested objects, common formats (email, uri, date, date-time, uuid), and returns a schema compatible with json_schema_validate. No contradictions.

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?

Three sentences front-loaded with the core action, followed by capabilities and use case. No redundancy, every sentence earns its place. Highly concise and well-structured.

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 and the presence of an output schema, the description covers input nature, detection capabilities, and typical use cases. It lacks mention of error handling or format limitations but is overall adequate for an agent to understand and invoke.

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 100%, so baseline is 3. The description adds minimal parameter-specific meaning; it implies 'input' is a sample JSON value but does not elaborate on the 'required_all' parameter behavior. The schema itself already documents these.

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 infers a JSON Schema from a sample JSON value, specifying verb and resource. It distinguishes from siblings like json_schema_validate and json_diff by focusing on generation rather than validation or diffing.

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 says 'Use when you have a sample API response or LLM output and want to auto-generate a validation schema for CI/CD testing,' providing clear context. However, it does not contrast with alternatives or state when not to use, which would strengthen guidance.

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