How to Use the Surfer SEO MCP in LlamaIndex

Q: How do I store Surfer SEO results with LlamaIndex?

You run an audit using createseoaudit and then index the resulting JSON data. This makes every finding, from keyword volume to score, part of your searchable vector store.

Q: Is this server good for historical analysis with LlamaIndex?

It's perfect. You can pull old audit records using listseoaudits and treat them as foundational documents in a Retrieval-Augmented Generation (RAG) pipeline.

Index Surfer SEO audit results and guidelines into a searchable knowledge base with LlamaIndex.

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…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Surfer SEO MCP to LlamaIndex

Create your Vinkius account to connect Surfer SEO to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Surfer SEO with LlamaIndex

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Build RAG applications using the MCP Server

Instead of just running an audit, use `create_seo_audit` and index the full results. This means your agent can query past data—like specific keyword guidelines or competitor terms—and get answers grounded in actual API reports.

Retrieving Surfer SEO Data for LlamaIndex

When you use `get_content_editor_details`, the output gives you full context and actionable rules. Indexing this data means your knowledge base can answer questions like, 'What were our guidelines for Topic X last month?'

Tracking SEO Performance with LlamaIndex

The `list_content_editors` tool lets you pull a list of all previous content editor queries. By indexing this data, your agent can track trends and compare performance across months without needing to re-run the audit.

Setup guide

Set up Surfer SEO 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 Surfer SEO 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 Surfer SEO tools.",
)
response = await agent.run("List recent Surfer SEO 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 Surfer SEO. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Surfer SEO now

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Surfer SEO MCP in LlamaIndex

You run an audit using `create_seo_audit` and then index the resulting JSON data. This makes every finding, from keyword volume to score, part of your searchable vector store.

This MCP Server touches URLs, content scores, and prominent terms. You index these specific text outputs so you can query them later for semantic understanding.

Yeah. Run an audit, get the raw results, and then feed those structured results into LlamaIndex to build a permanent knowledge source for your team.

It's perfect. You can pull old audit records using `list_seo_audits` and treat them as foundational documents in a Retrieval-Augmented Generation (RAG) pipeline.

It manages structured text like audit reports, content guidelines, and SERP search results. These are the exact pieces you want to make searchable for your agent.

Use it with your favorite AI tools

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