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

No annotations are provided, so the description carries the full burden. It discloses the return shape (valid/invalid plus violations with JSON Pointer locations and messages) and payment terms ($0.01/call in USDC on eip155:8453), but omits rate limits, input-size limits, and explicit read-only/no-side-effect confirmation.

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 front-loaded with the core operation and return behavior. The third sentence repeats the usage condition already implied by the first, creating minor redundancy, while the price sentence is necessary for invocation.

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?

With no output schema and no annotations, the description does real work by explaining return values, version expectations, and payment. It is complete enough for a two-parameter read tool, though edge-case behavior for malformed input is not covered.

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%, and the input schema gives only terse descriptions ('JSON data', 'JSON Schema'). The tool description adds meaningful constraint by specifying draft 2020-12 for the schema parameter, exceeding the baseline 3.

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?

States a specific verb, resource, and version: 'Validate a JSON document against a JSON Schema (draft 2020-12).' The purpose is clearly distinct from sibling tools like dev_json_formatter and dev_json_repair.

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?

'Use this when an agent needs to validate JSON against a draft 2020-12 schema' gives a clear usage condition. It does not name when-not conditions or alternatives, but no sibling performs schema validation.

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