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

Beyond annotations (readOnlyHint, idempotentHint), the description adds critical behavioral context: it is a validity gate, rejects invalid input with exact parse error, and never repairs, completes, or guesses. This gives clear expectations for edge cases without contradicting 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 efficiently structured: front-loaded main purpose, followed by strictness details, then specific exclusions. Every sentence earns its place with actionable information, and the use of em-dashes and 'NOT for' makes it highly scannable.

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 simplicity, complete schema, and available annotations, the description fully covers purpose, usage, exclusions, and behavior. The presence of an output schema means return values are already documented, so no further explanation is needed.

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 the baseline is 3. The description does not add significant parameter-level meaning beyond the schema, though it reinforces the strict parsing requirement. The schema already documents both parameters thoroughly, so no compensation is needed.

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's function: validate and pretty-print a string that is already valid JSON. It specifies the resource (JSON string), the action (validate/pretty-print), and the strict scope, distinguishing it from siblings like extract_json_from_text and yaml_to_json.

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?

Explicitly provides when-to-use and when-not-to-use guidance, including specific alternatives for other cases: extract_json_from_text for JSON embedded in prose, JSON.stringify for JS objects, yaml_to_json for YAML, and notes that plain text will fail. This is exemplary usage guidance.

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