How to Use the Google Lighthouse SEO Auditor MCP in LlamaIndex

Q: How do I index Google Lighthouse SEO Auditor data in LlamaIndex?

You call the runlighthouseaudit tool through the LlamaIndex MCP tool spec and pass the resulting JSON to your document parser. From there, the performance scores and diagnostic text are indexed into your vector store for RAG workflows.

Q: Can I use Google Lighthouse SEO Auditor with LlamaIndex query engines?

Yes, you can expose the runlighthouseaudit tool to a FunctionAgent. The agent executes the audit, and the query engine can retrieve those live metrics alongside your indexed local web documentation to answer complex technical questions.

Index live PageSpeed diagnostics into LlamaIndex to query your site's SEO history without hallucinations.

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Connect Google Lighthouse SEO Auditor MCP to LlamaIndex

Create your Vinkius account to connect Google Lighthouse SEO Auditor 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 Google Lighthouse SEO Auditor with LlamaIndex

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Index live Lighthouse audits into LlamaIndex

The `run_lighthouse_audit` tool lets your LlamaIndex agent pull live PageSpeed metrics directly into your data pipeline. When your agent runs the audit, LlamaIndex captures the performance scores and failed diagnostics, turning them into document nodes. These nodes are immediately indexed into your vector database. This MCP integration lets you query your site's technical health using semantic search. Instead of re-running tests, you can ask your agent what caused the layout shift last week and get an answer backed by historical audit data.

Build RAG pipelines for performance optimization

Running the `run_lighthouse_audit` tool lets LlamaIndex pull failed diagnostics and match them against your local documentation. Your agent does not just suggest generic fixes. It looks at the specific render-blocking JS error, searches your indexed codebase pattern library, and writes a tailored fix that fits your exact architecture. This keeps your code clean. You avoid standard copy-paste fixes from the web that do not match your project's design system or coding standards.

Query historical audit trends with semantic search

This MCP Server exposes raw SEO metrics via `run_lighthouse_audit` that LlamaIndex structures for long-term retrieval. You can track performance regressions over time by asking your agent to compare today's audit results with last month's indexed JSON. This approach eliminates the need for complex charting dashboards. You interact with your performance history through natural language, pinpointing exactly when a heavy image payload first degraded your mobile load speed.

Setup guide

Set up Google Lighthouse SEO Auditor 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 Google Lighthouse SEO Auditor 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 Google Lighthouse SEO Auditor tools.",
)
response = await agent.run("List recent Google Lighthouse SEO Auditor data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Google PageSpeed. 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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Common questions about Google Lighthouse SEO Auditor MCP in LlamaIndex

You call the `run_lighthouse_audit` tool through the LlamaIndex MCP tool spec and pass the resulting JSON to your document parser. From there, the performance scores and diagnostic text are indexed into your vector store for RAG workflows.

Yes, you can expose the `run_lighthouse_audit` tool to a FunctionAgent. The agent executes the audit, and the query engine can retrieve those live metrics alongside your indexed local web documentation to answer complex technical questions.

By feeding real-time JSON metrics directly into the context window, the agent relies on actual PageSpeed data instead of guessing. Your LlamaIndex application bases its code recommendations strictly on the failed audits returned by the API.

No, the server queries Google's public PageSpeed API. This keeps your indexing pipelines fast and lightweight, as you do not need to run or configure local Chrome instances on your server.

The server only reads the target URLs you submit to fetch PageSpeed scores. Since Vinkius hosts this MCP Server in a zero-trust, ephemeral sandbox, your diagnostic data is isolated and cleared as soon as the execution finishes.

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