How to Use the Elastic Enterprise Search MCP in LlamaIndex

Q: How does LlamaIndex perform a search with Elastic Enterprise Search?

LlamaIndex uses the search tool to fetch data. It then incorporates that data into its vector store for semantic retrieval.

Q: Can I feed Elastic Enterprise Search data into a LlamaIndex RAG flow?

Absolutely. You use the listdocuments tool to pull existing content and then index it within LlamaIndex for your agent to reference.

Q: Is it possible to track search trends with Elastic Enterprise Search and LlamaIndex?

Yes, you pull performance data with the analytics tool. This helps you understand what your users are actually searching for.

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Connect Elastic Enterprise Search MCP to LlamaIndex

Create your Vinkius account to connect Elastic Enterprise Search 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 Elastic Enterprise Search with LlamaIndex

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Vector-ready document indexing

Use `index_documents` to push fresh content into your Elastic engines. LlamaIndex then treats these entries as a source of truth for your knowledge base. You avoid stale data by keeping your index in sync with live API updates. The agent handles the ingestion, ensuring your RAG pipeline stays current.

Contextual search retrieval

The `search` tool retrieves specific documents for your agents to process. LlamaIndex indexes these results to provide grounded answers to user questions. You aren't just getting raw text back. You get structured data that the agent uses to build a more accurate, context-aware response.

Automated engine discovery

Use `list_engines` to let your agent map out your available search infrastructure. It identifies the correct target engine for any given query automatically. This makes your RAG applications more flexible. You can add or modify engines in Elastic without rewriting your agent's core logic.

Setup guide

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

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Common questions about Elastic Enterprise Search MCP in LlamaIndex

LlamaIndex uses the `search` tool to fetch data. It then incorporates that data into its vector store for semantic retrieval.

Absolutely. You use the `list_documents` tool to pull existing content and then index it within LlamaIndex for your agent to reference.

Yes, you pull performance data with the `analytics` tool. This helps you understand what your users are actually searching for.

The server treats your data as transient. It passes through the secure V8 sandbox and is never cached or stored on our side.

Access is controlled solely by your Elastic API keys. We only provide the conduit for the agent to talk to your private engine.

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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