How to Use the Raven Tools MCP in LlamaIndex

Index your live SEO metrics with LlamaIndex by turning Raven Tools data into a searchable knowledge base.

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…and any MCP-compatible client

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Connect Raven Tools MCP to LlamaIndex

Create your Vinkius account to connect Raven Tools 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 Raven Tools with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Index SEO rankings with LlamaIndex

Fetch keyword data using `get_rank_all` and add it to your vector index. This allows your agent to query historical ranking performance based on semantic meaning rather than just raw numbers. Your RAG application stays current by refreshing the index with new outputs from `get_rank`. It grounds your AI responses in real-time search data.

Query site audit results in LlamaIndex

Run `get_site_audit` to retrieve technical health data and pipe it into your knowledge base. You can ask questions about your site's crawlability and get answers derived from the latest audit report. This method keeps your documentation synchronized with actual site performance. The index makes complex audit structures easy to query.

Manage backlink data via LlamaIndex

Incorporate `get_links` into your agentic workflow to build a searchable profile of your link graph. You can ask your agent to compare current link health against past data stored in your index. This approach turns raw link lists into actionable intelligence. Your agent uses this context to prioritize outreach or content updates.

Setup guide

Set up Raven Tools 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 Raven Tools 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 Raven Tools tools.",
)
response = await agent.run("List recent Raven Tools 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 Raven Tools. 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.

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Real-time monitoring

Live

visibility into every interaction

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

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One

place for every integration

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Common questions about Raven Tools MCP in LlamaIndex

You use the MCP tool spec to wrap the Raven Tools interface. Once connected, your agent treats the API as a source for document retrieval and knowledge base updates.

Yes, by indexing the tool outputs. You query your local vector store, and the agent uses the tools to pull the most recent stats if the index is stale.

Focus on ranking trends and site audit summaries. These provide the most value when retrieved semantically to answer questions about long-term SEO health.

The server supports asynchronous requests, which helps when loading large site audits into your index. This prevents blocking your main agent loop.

We isolate your SEO project data within the Vinkius sandbox. Only your specific keyword lists and domain audit metrics are processed, ensuring they never leave your secure environment.

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