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Validate structured data

validate_structured_data
Read-onlyIdempotent

Validate pasted JSON-LD against the schema.org vocabulary (every type, property, domain, range and enumeration value) and Google's documented rules for LocalBusiness and Organization markup: required name and address, recommended properties, hh:mm:ss hours, 5 decimal coordinates, priceRange length, self-serving reviews, sameAs. Accepts one block, several blocks, or the script tags around them. Each issue cites its source. No network access.

Input Schema

NameRequiredDescriptionDefault
json_ldYesJSON-LD text. Script tags are stripped; several script blocks are each validated.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and openWorld=false; the description reinforces the safety profile with 'No network access' and adds genuinely new behavioral context: validation is deterministic against cited sources ('Each issue cites its source'). It stops short of describing issue severity or output shape.

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

Conciseness4/5

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

One dense sentence with the purpose front-loaded, followed by a rule inventory. The long enumeration of checks is justified because it defines the tool's scope, though it could be trimmed slightly without losing meaning.

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

Completeness4/5

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

For a single-parameter, no-output-schema tool, the description enumerates the validation rule set, states accepted input forms, and hints at the return ('each issue cites its source'). Nothing essential is missing, though the issue object's structure is left unspecified.

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

Parameters3/5

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

Schema coverage is 100% and the single parameter's description already explains script-tag stripping and multi-block handling, so the description's matching sentence is largely redundant. Baseline 3 for a fully documented single parameter.

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?

Specific verb+resource ('Validate pasted JSON-LD against the schema.org vocabulary and Google's rules') with the validation scope spelled out. It is clearly distinct from siblings like generate_local_business_schema or check_entity_consistency, which produce or compare markup rather than validate it.

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

Usage Guidelines3/5

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

Input-format guidance is present ('Accepts one block, several blocks, or the script tags around them'), which tells the agent what to pass. However, there is no explicit when-to-use statement or routing against siblings (e.g., validate before/after generate_local_business_schema), so usage is only implied.

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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