Generate JSON-LD schema

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

Beyond annotations (readOnlyHint, openWorldHint), the description discloses the subscription requirement, free trial details, output format ('Returns valid JSON-LD'), and a fallback action. It adds real behavioral context without contradicting the 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 informative but not wasteful; it front-loads purpose, then covers usage and prerequisites. It's slightly longer than the minimal two-sentence ideal but every sentence serves a purpose.

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 simple parameter structure and no output schema, the description fully covers what the tool does, when to use it, what it returns, and critical prerequisites. The mention of the alternative get_sample_audit completes the picture within the tool ecosystem.

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 covers both parameters with clear descriptions (type enum and domain example), yielding 100% schema description coverage. The tool description adds no additional parameter-level meaning beyond stating the general purpose, so the baseline score of 3 is appropriate.

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 a specific verb ('Generate') and resource ('JSON-LD schema') with context ('ready-to-paste structured data tailored to a website'). It distinguishes from sibling tools by focusing on schema generation rather than audits or tracking, and explicitly names related queries ('schema,' 'structured data,' 'JSON-LD').

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 states when to use the tool ('Use this when someone asks for "schema," "structured data," "JSON-LD," or wants the actual markup') and provides a clear alternative path when prerequisites aren't met ('if the user doesn't have one, call get_sample_audit first'). This is textbook when/when-not/alternative guidance.

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