JSON-LD SEO Compiler MCP for AI. Generate perfectly structured markup for Google search results.

Q: What data does buildschema accept as input?

buildschema accepts a JSON string containing the core properties of your content, plus you must specify the overall Schema.org type (like Article or Product).

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

JSON-LD SEO Compiler automatically builds 100% Google-compliant structured data blocks. It takes raw content metadata and deterministically injects all required Schema.org boilerplate, preventing AI agents from generating invalid or incomplete schema markup that search engines reject.

What your AI can do

Build schema

Takes a content type and properties, then automatically generates a fully compliant JSON-LD block for SEO structured data.

Generate Article Schema

Creates valid JSON-LD blocks specifically designed for blog posts or informational articles.

Format Product Data

Compiles product attributes—like price, SKU, and availability—into structured data Google understands.

Validate Schema Structure

Automatically injects mandatory boilerplate (like @context and @type) to ensure the schema meets strict Google guidelines.

Ask an AI about this

Included with Plan

Waiting for input…

AI Agent

JSON-LD SEO Compiler: 1 Tool Available

Use these tools to build correct Schema.org types and JSON strings for guaranteed structured data accuracy.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using JSON-LD SEO Compiler on Vinkius

Build Schema

Takes a content type and properties, then automatically generates a fully compliant JSON-LD block for SEO structured data.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The JSON-LD SEO Compiler integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "json-ld-seo-compiler": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the JSON-LD SEO Compiler tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"json-ld-seo-compiler": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with JSON-LD SEO Compiler, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Native JSON Engine. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

The Challenge of Publishing Structured Data

Right now, when an AI agent writes a product description or article, the process involves multiple steps: gathering metadata, formatting it into JSON structure, and then manually ensuring that critical boilerplate—like defining the context or specifying the type—is included. It's high-friction work prone to human error.

With this MCP, you skip all those manual checks. You feed in simple data points, and the system outputs a single, flawless script block. What changes is reliability. The process moves from 'hope it works' to 'it always works'.

Using the JSON-LD SEO Compiler

The manual steps that disappear are the need to cross-reference dozens of Schema.org guidelines, manually injecting `@context` declarations, and running multiple pre-publish validation checks. You stop worrying about syntax errors.

Now, your agents can operate faster and more confidently. The output is not just data; it's a guarantee that search engines will correctly interpret the information.

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What your AI can actually do with this

When an agent generates a blog post, product page, or FAQ section for a CMS, it needs more than just text; it needs structure. Without perfect structured data, Google might ignore the content's context—your rich snippets just won't appear. The JSON-LD SEO Compiler solves this structural headache. It acts as a validator and formatter in one.

You feed it simple metadata, like a headline or product price, and it outputs a ready-to-use, perfectly formatted <script type="application/ld+json"> block. This means your agent can generate content with confidence, knowing the SEO markup is flawless every time. If you're building complex systems, connecting this MCP through Vinkius gives you access to specialized tools for data validation right where you need it.

Built · Hosted · Managed by Vinkius JSON-LD SEO Compiler - Structured Data Validation MCP

Server ID 019e38b1-39df-7166-a18f-eb78540c717b

Vinkius Inspector

Compliance Grade F

Score 3.6/100

Report View Report ↗

Here's how it actually works

The bottom line is you give it raw data and the intended content type; it gives back guaranteed Google-compliant structured markup.

Pass the compiler a simple JSON payload containing your content's core metadata. You must also specify the Schema.org type (e.g., Article, Product).

The MCP processes this data, deterministically injecting all required boilerplate and attributes that an AI agent might forget.

You receive a perfectly formatted, ready-to-inject <script type="application/ld+json"> block for direct HTML placement.

What Changes When You Connect

Stops AI hallucination of bad data. The build_schema tool enforces mandatory attributes like @context and @type, ensuring every output block is valid from the start.

Saves time checking schemas manually. Instead of running multiple validation tools, this MCP compiles everything into one ready-to-use script tag.

Ensures content visibility. By guaranteeing compliant structured data for articles or products, your site maximizes its chances of earning rich snippets in search results.

Handles complex types easily. You can pass simple metadata and the tool handles the complexity of different Schema.org models (e.g., Article vs. FAQPage).

Integrates into any workflow. Because it outputs a clean, standard script block, you just drop it directly into your CMS template layer.

The honest tradeoffs

Relying on LLMs for Schema

Anti-pattern

Asking your agent to 'just write the JSON-LD schema' based only on text prompts. This often results in missing mandatory attributes or using incorrect @type values, leading to invalid schemas that Google ignores.

The Fix

You must pass the data through this MCP and use the build_schema tool. Always specify the Schema.org type first—it forces the system to build a fully validated structure.

Manual Schema Assembly

Anti-pattern

Copying and pasting boilerplate tags from documentation into your code, leading to structural inconsistencies or outdated schema versions.

The Fix

Use this MCP. It handles the latest Schema.org standards automatically for you, ensuring the markup is technically correct every time.

Questions you might have

How does JSON-LD SEO Compiler help with Google compliance? +

The tool enforces strict adherence to Schema.org standards by automatically including mandatory attributes like @context and @type. This prevents the kind of incomplete data that search engines reject.

Do I need to use `build_schema` for every content type? +

No, but you must use it if structured data is critical. The tool accepts different Schema types (Article, Product, etc.), so you only run what's necessary.

Is JSON-LD SEO Compiler better than just asking my agent to write the schema? +

Yes. Asking your agent is prone to 'hallucination' of invalid syntax or missing attributes. This MCP guarantees structural integrity every time, which is what you need for production.

What data does `build_schema` accept as input? +

build_schema accepts a JSON string containing the core properties of your content, plus you must specify the overall Schema.org type (like Article or Product).

What happens if I run `build_schema` with malformed or incomplete data? +

The compiler validates your input JSON syntax first. If there are structural errors, the tool will throw a specific error message identifying the problematic field. This prevents you from injecting invalid code into your CMS.

Can I use the JSON-LD SEO Compiler for multiple content types in one go? +

The compiler generates individual, distinct schema blocks. For example, if you have product and article data, you run build_schema sequentially for each type. You then combine these separate script tags.

If my source JSON is missing required attributes (like a publication date), will the compiler fill them in? +

No, the tool does not hallucinate core data points; you must provide all factual information. However, it handles the schema boilerplate—injecting necessary attributes like @context and correctly formatting the structure for Google.

Are there performance limits or rate restrictions when using the JSON-LD SEO Compiler? +

The MCP is built for high throughput. Usage rates are governed by Vinkius platform policies, but it processes schema compilation very quickly, even with large datasets.

Why do I need an engine for this? +

LLMs can easily break JSON syntax (missing quotes, trailing commas) which causes HTML parsers to crash. This engine parses and stringifies deterministically.

Does it support all Schema types? +

Yes! You can pass any valid Schema.org type like Article, Product, FAQPage, or LocalBusiness.

Can it nest objects? +

Yes, just pass a nested JSON payload and the engine will compile it correctly.

Connect to your AI in seconds.

Build schema

JSON-LD SEO Compiler: 1 Tool Available

Make your AI actually useful.

Build Schema

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

The Challenge of Publishing Structured Data

Using the JSON-LD SEO Compiler

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Bulk Product Page Generation

Automated Blog Publishing

FAQ Section Integration

The honest tradeoffs

Relying on LLMs for Schema

Manual Schema Assembly

When It Fits, When It Doesn't

Questions you might have