How to Use the JSON-LD SEO Compiler MCP in LlamaIndex

Q: How do I register the JSON-LD SEO Compiler with LlamaIndex?

Install llama-index-tools-mcp and instantiate BasicMCPClient with the server endpoint. Wrap it in McpToolSpec to extract the buildschema tool for your function agent.

Q: Can LlamaIndex index the outputs of the JSON-LD SEO Compiler?

Yes. The generated JSON-LD blocks from the buildschema tool can be loaded directly into a document object and stored in your vector index for later retrieval.

Index valid JSON-LD schemas directly into your LlamaIndex vector store for search-engine optimization.

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Connect JSON-LD SEO Compiler MCP to LlamaIndex

Create your Vinkius account to connect JSON-LD SEO Compiler to LlamaIndex 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 LlamaIndex

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Index Validated SEO Metadata for RAG

LlamaIndex works best when its knowledge base is clean. Using the `build_schema` tool ensures your agent generates correct JSON-LD blocks that you can index straight into your vector store. No more broken markup cluttering your semantic index. When your RAG pipeline retrieves information, it can query these indexed schema blocks. This keeps your search-engine optimization data grounded in actual API outputs rather than LLM-guessed structures.

Query Past SEO Configurations with LlamaIndex

Stop rebuilding schemas from scratch. By indexing the outputs of the `build_schema` tool, your LlamaIndex pipeline can query previous configurations. It finds existing patterns and reuses them instantly. The agent checks the vector store to see if a similar Product or Article schema already exists. If it does, it pulls the structure, updates the values, and spits out a validated block without starting over.

Filter and Control Tools in LlamaIndex

You do not want your agents running wild with too many tools. Use LlamaIndex's allowed tools filter to restrict your agent's access specifically to the `build_schema` tool on this MCP Server when compiling metadata. This prevents the agent from calling unnecessary API endpoints. It keeps token usage down and ensures your pipeline focuses solely on generating valid structured data blocks for your web pages.

Setup guide

Set up JSON-LD SEO Compiler MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all JSON-LD SEO Compiler MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to JSON-LD SEO Compiler tools.",
)
response = await agent.run("List recent JSON-LD SEO Compiler data")

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Common questions about JSON-LD SEO Compiler MCP in LlamaIndex

Install `llama-index-tools-mcp` and instantiate `BasicMCPClient` with the server endpoint. Wrap it in `McpToolSpec` to extract the `build_schema` tool for your function agent.

Yes. The generated JSON-LD blocks from the `build_schema` tool can be loaded directly into a document object and stored in your vector index for later retrieval.

Raw models frequently hallucinate invalid attributes. This MCP Server guarantees that the output of `build_schema` is 100% compliant with Google's structured data guidelines every single time.

Yes. You can call the tool list asynchronously in LlamaIndex using `to_tool_list_async`. This prevents blocking your main thread when compiling multiple schemas for large-scale web directories.

The MCP Server processes your raw JSON schema objects ephemerally in memory. It executes in a zero-trust sandbox and immediately discards the data after returning the compiled HTML script block.

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