How to Use the Elevio MCP in LlamaIndex

Build RAG applications in LlamaIndex that index your live Elevio knowledge base for grounded, accurate AI responses.

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Connect Elevio MCP to LlamaIndex

Create your Vinkius account to connect Elevio 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 Elevio with LlamaIndex

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Index Live Elevio Content in LlamaIndex

Turn your knowledge base into a searchable vector index by feeding the output of `list_knowledge_articles` into your LlamaIndex pipeline. This grounds your agent in real-time data. Your system stops guessing because it uses live API data to build the context. It creates a unified source of truth for all customer-facing inquiries.

Contextual Search and Retrieval

Use `search_knowledge_articles` to pull relevant documentation into your index on demand. This ensures your RAG pipeline always reflects the latest article content. Your agent fetches exactly what it needs to answer questions. It avoids hallucinations by anchoring every response to your actual knowledge base.

Automated Knowledge Monitoring

Monitor `list_latest_article_updates` to keep your vector store current. When articles change, your index updates to match the new source of truth. This keeps your retrieval system in sync with reality. You stop serving old information to users who need the latest documentation.

Setup guide

Set up Elevio 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 Elevio 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 Elevio tools.",
)
response = await agent.run("List recent Elevio data")

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Common questions about Elevio MCP in LlamaIndex

Absolutely. Use the McpToolSpec to pull articles and ingest them into your vector store for semantic search.

Yes. By indexing the output of the tools, your search becomes much smarter than simple keyword matching.

The server provides the data interface. You then use the standard LlamaIndex ingestion flow to turn that data into queryable vectors.

You can. Use the tool outputs to select only the articles that meet your criteria before passing them to the indexer.

The data stays within your controlled environment. Only the specific articles you choose to index are pulled into your vector store.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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