How to Use the JSON-LD SEO Compiler MCP in Pydantic AI

Q: How do I add the JSON-LD SEO Compiler to Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset with your Vinkius HTTP endpoint, and pass it to your Agent's toolsets array.

Fail loudly on broken structured data. The JSON-LD SEO Compiler brings strict type safety to your Pydantic AI agents.

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Connect JSON-LD SEO Compiler MCP to Pydantic AI

Create your Vinkius account to connect JSON-LD SEO Compiler to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup JSON-LD SEO Compiler with Pydantic AI

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Type-safe schemas via MCP Server.

Pydantic AI prioritizes exact execution over vague generative outputs. When you connect the JSON-LD SEO Compiler MCP server, your agent gains a deterministic engine for formatting Schema.org blocks. The agent extracts the necessary data and calls the `build_schema` tool. The compiler automatically handles the @context and @type injection, returning a valid <script type="application/ld+json"> block that your application can immediately inject into the DOM.

Zero silent corruption for SEO.

Search engines ignore invalid JSON-LD, meaning a silent hallucination costs you organic traffic. This MCP Server forces the LLM to separate data extraction from syntax formatting. If the underlying model tries to pass a malformed JSON string of properties to the compiler, Pydantic AI catches the validation error instantly. Your system fails loudly during the generation step instead of silently publishing broken markup to production.

Model-agnostic schema compilation.

Because Pydantic AI works across different providers, you need tools that behave consistently regardless of the LLM. The compiler provides that stable baseline. You can swap your agent's underlying model to a cheaper or faster alternative without worrying about schema formatting regressions. The `build_schema` tool executes exactly the same way every time, ensuring your SEO metadata remains intact.

Setup guide

Set up JSON-LD SEO Compiler MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "json-ld-seo-compiler-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to JSON-LD SEO Compiler tools.",
)

result = await agent.run("List recent JSON-LD SEO Compiler transactions")
print(result.output)

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Common questions about JSON-LD SEO Compiler MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an MCPToolset with your Vinkius HTTP endpoint, and pass it to your Agent's toolsets array.

The framework validates the tool call arguments before execution. If the agent fails to provide the required Schema.org type or a valid JSON string of properties, Pydantic AI throws a runtime error.

No. The MCP Server standard and Pydantic AI's architecture are model-agnostic. You can use any supported model to extract the properties and call the compiler.

The compiler ensures the structural integrity of the JSON-LD block, injecting the mandatory wrapper elements. You still define the specific properties based on your target schema type.

The server receives your raw page data, like article content and organization details. Execution happens in an ephemeral V8 sandbox with zero-trust architecture, discarding your data the millisecond the compiled script block is returned.

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