Generate JSON-LD schema

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

Annotations already indicate readOnly/openWorld/non-destructive behavior, so the description correctly avoids repeating that. It adds substantial beyond-schema context: a subscription requirement, trial terms, the promise of valid JSON-LD output, and a fallback tool. This meaningfully helps the agent manage user expectations.

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 action, then efficiently lists trigger phrases, the output guarantee, and the subscription/trial caveat. Although long, every clause earns its place because the paywall detail directly affects invocation decisions.

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?

For a simple two-parameter tool with full schema coverage and strong annotations, this description is complete. It covers when to use it, what it returns, access requirements, and the non-subscriber fallback. No output schema exists, but 'Returns valid JSON-LD' is sufficient for the tool's simple return type.

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 describes both parameters fully (domain example, type enum with auto-detect), giving 100% schema description coverage. The description adds only the general idea that output is tailored to the website, so it does not significantly advance parameter understanding beyond the schema baseline.

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 states a specific verb ('Generate'), a concrete deliverable ('ready-to-paste structured data (JSON-LD schema)'), and the intended effect on AI and search-engine understanding. It also gives clear trigger phrases, which helps an agent distinguish this tool from siblings like get_sample_audit.

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?

It explicitly defines when to use the tool: when the user asks for 'schema,' 'structured data,' 'JSON-LD,' or wants the actual markup. It also names a condition and an alternative: if the user lacks a subscription, call get_sample_audit first. This is exact routing guidance.

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