How to Use the Elasticsearch Vector MCP in LlamaIndex

Q: How does LlamaIndex use the Elasticsearch Vector MCP Server for retrieval?

Your LlamaIndex query engine calls the search tool to run a kNN query against your dense vector index. The returned documents are converted into node objects for response synthesis.

Q: Can I index LlamaIndex document chunks directly into Elasticsearch Vector?

Yes. Use the indexdocument tool inside your ingestion pipeline. It maps the LlamaIndex document text and its corresponding embedding vector directly to your index.

Q: How does LlamaIndex verify index schemas before querying Elasticsearch Vector?

The engine calls getindex to inspect the dense vector mapping. This ensures the vector dimensions in Elasticsearch match the embeddings generated by LlamaIndex.

Q: What is the best way to handle multiple vector indexes in LlamaIndex?

Use listindexes to get a complete list of your vector stores. Your LlamaIndex router agent can then dynamically choose which index to target based on the user's query.

Index live Elasticsearch Vector search results directly into your LlamaIndex knowledge base with this managed MCP server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Elasticsearch Vector MCP to LlamaIndex

Create your Vinkius account to connect Elasticsearch Vector 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.

GDPR Free for Subscribers

Setup Elasticsearch Vector with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build live RAG indexes using LlamaIndex

The `search` tool pulls dense vector matches directly into your LlamaIndex query engine to ground response generation. Your agent queries the Elasticsearch cluster, retrieves the top documents, and immediately indexes them into local memory. This prevents the agent from hallucinating data. By running vector search on live data, your LlamaIndex application combines real-time database records with your local document store.

Write embeddings from LlamaIndex pipelines

The `index_document` tool writes new vector embeddings generated by LlamaIndex node parsers straight into your cluster. As your ingest pipeline processes new PDFs or text files, it pushes the dense vectors directly to the search index. You don't need a separate ingestion script. The LlamaIndex agent handles chunking, embedding generation, and indexing in one continuous pipeline step.

Keep LlamaIndex knowledge bases clean

The `delete_document` tool removes stale vector records from your Elasticsearch cluster when LlamaIndex detects outdated source files. Your agent compares local document states with the index and purges dead vectors instantly. This ensures your query engine never pulls obsolete context. You can also use `list_indexes` to check which vector stores are active before starting a synchronization run via this MCP setup.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Elasticsearch Vector. 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 Elasticsearch Vector now

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

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 Elasticsearch Vector MCP in LlamaIndex

Your LlamaIndex query engine calls the `search` tool to run a kNN query against your dense vector index. The returned documents are converted into node objects for response synthesis.

Yes. Use the `index_document` tool inside your ingestion pipeline. It maps the LlamaIndex document text and its corresponding embedding vector directly to your index.

The engine calls `get_index` to inspect the dense vector mapping. This ensures the vector dimensions in Elasticsearch match the embeddings generated by LlamaIndex.

Use `list_indexes` to get a complete list of your vector stores. Your LlamaIndex router agent can then dynamically choose which index to target based on the user's query.

Yes, Vinkius executes all MCP server operations in an ephemeral, zero-trust sandbox. Your vector embeddings and document properties are never cached or logged on Vinkius servers during transport.

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