Validate JSON-LD Schema

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds meaningful behavioral detail: it extracts every script block, runs each through a rules engine covering 20+ types, and reports required-field errors, warnings, and type-specific gotchas. This goes beyond the annotations without contradicting them.

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 two sentences with no redundancy. It front-loads the primary purpose, then details the scope, process, and output in a compact format. Every sentence earns its place.

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 single-parameter, read-only tool with no output schema, the description is complete. It explains what is validated, how (extraction and rules engine), and what is reported (errors, warnings, gotchas). An agent has enough to call it correctly without additional context.

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?

With only one parameter (url) and 100% schema description coverage, the schema already fully documents the parameter. The description's mention of 'on a URL' adds no new syntax or format details. Per the rubric, the baseline of 3 is appropriate since the schema carries the heavy lifting.

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 (validate) and resource (all JSON-LD structured data on a URL), and lists the covered types (Article, Organization, Product, etc.), which clearly distinguishes it from sibling tools like generate_schema or validate_robots. It leaves no ambiguity about what the tool does.

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 clearly implies when to use this tool: whenever you need to validate JSON-LD structured data on a given URL. However, it does not explicitly mention alternatives or state when not to use it (e.g., for generating schema or validating robots.txt). The context is clear but lacks explicit exclusions.

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