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audit_schema

Validate JSON-LD structured data against Schema.org rules and AI-citation best practices. Accept a URL or raw JSON string for offline or online audit.

Instructions

Validate JSON-LD structured data against Schema.org rules and AI-citation best practices. Accepts either a URL (fetched) or a raw JSON string (parsed directly).

Read-only when given url (one HTTP GET). Zero network when given schema_json. No writes.

Deterministic, rule-based; no LLM. Validates required/recommended properties, @context correctness, sameAs links, and AI-search-friendly patterns.

When to use: focused JSON-LD audits, or to validate a schema block you're about to ship. For a full page audit that includes schema + everything else, use audit_page instead.

Either url or schema_json must be provided (not both). If both are provided, schema_json wins and no fetch happens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublic URL to fetch and audit. Either this OR `schema_json` is required. Read-only HTTP GET.
schema_jsonNoRaw JSON-LD as a string (the contents of a `<script type="application/ld+json">` block). Use this to validate a schema block offline without fetching a URL. Either this OR `url` is required.
respect_robotsNoIf true (default), respect robots.txt before fetching `url`. Ignored when `schema_json` is used.
Behavior5/5

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

Despite no annotations, description fully discloses behavior: read-only GET for url, zero network for schema_json, no writes, deterministic, rule-based, no LLM. Also mentions robots.txt handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise and well-structured: purpose first, then operational details, usage guidelines, and parameter notes. No unnecessary sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description covers all necessary aspects: input constraints, behavioral notes, use cases, and sibling differentiation. Complete for a validation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters have descriptions in schema (100% coverage). Description adds clarifying rules: mutually exclusive parameters, schema_json wins when both provided, and robots ignored when schema_json used.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it validates JSON-LD structured data against Schema.org rules and AI-citation best practices. It distinguishes from sibling `audit_page` by specifying the scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use: 'focused JSON-LD audits' or 'validate a schema block you're about to ship'. Provides alternative: 'audit_page' for full page audit. Also clarifies parameter constraints.

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

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