Can my agent perform kNN searches using raw vector arrays?

Yes. Use the 'search' tool. Provide the index name and a JSON array representing your query vector. The agent will perform raw K-Nearest Neighbors computations to find the most semantically similar documents.

How do I create a new vector index with specific dimensions via chat?

Use the 'create_index' tool. You can specify the index name and the number of dimensions (e.g., 1536 for OpenAI embeddings). The agent will provision the strictly typed data structure in your Elasticsearch cluster.

Can I delete a single document from a vector index through the agent?

Absolutely. Use the 'delete_document' tool with the index and document ID. The agent will enforce immediate document vaporization, stripping the record from the physical Lucene partitions.

Elasticsearch Vector MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Elasticsearch Vector
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Elasticsearch Vector as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Elasticsearch Vector. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Elasticsearch Vector?"
    )
    print(response)

asyncio.run(main())

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About Elasticsearch Vector MCP Server

Connect your Elasticsearch cluster to any AI agent and take full control of your vector search and semantic discovery workflows through natural conversation.

LlamaIndex agents combine Elasticsearch Vector tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

AI-Powered Vector Search — Perform raw K-Nearest Neighbors (kNN) computations mapping absolute semantic similarity across multi-dimensional embedding arrays
Index Orchestration — Enumerate active storage namespaces and validate physical Elasticsearch clusters tracking explicit dimensional shards securely
Schema Management — Analyze specific index mapping rules and provision strictly typed data structures enforcing numeric dimensions for cluster readiness
Document Indexing — Command synchronous bulk insertions attaching exact dense_vector embedding payloads to persist data into raw Lucene partitions
Data Invalidation — Enforce immediate hard document vaporization finding specific exact UUIDs stripping records from physical indices seamlessly
Metadata Auditing — Analyze dimensional constraints and matching similarity thresholds perfectly to verify your vector search configurations

The Elasticsearch Vector MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Elasticsearch Vector to LlamaIndex via MCP

Follow these steps to integrate the Elasticsearch Vector MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Elasticsearch Vector

Why Use LlamaIndex with the Elasticsearch Vector MCP Server

LlamaIndex provides unique advantages when paired with Elasticsearch Vector through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Elasticsearch Vector tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Elasticsearch Vector tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Elasticsearch Vector, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Elasticsearch Vector tools were called, what data was returned, and how it influenced the final answer

Elasticsearch Vector + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Elasticsearch Vector MCP Server delivers measurable value.

Hybrid search: combine Elasticsearch Vector real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Elasticsearch Vector to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Elasticsearch Vector for fresh data

Analytical workflows: chain Elasticsearch Vector queries with LlamaIndex's data connectors to build multi-source analytical reports

Elasticsearch Vector MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Elasticsearch Vector to LlamaIndex via MCP:

create_index

Create dense_vector index

delete_document

Delete a document

get_index

Get index info

index_document

Index a document

list_indexes

List all indexes

search

Dense vector knn search

Example Prompts for Elasticsearch Vector in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Elasticsearch Vector immediately.

"Perform a kNN search in index 'product-embeddings' with vector [0.1, 0.2, ...]"

"Create a new vector index 'image-features' with 512 dimensions"

"List all vector indexes in my cluster"

Troubleshooting Elasticsearch Vector MCP Server with LlamaIndex

Common issues when connecting Elasticsearch Vector to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Elasticsearch Vector + LlamaIndex FAQ

Common questions about integrating Elasticsearch Vector MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Elasticsearch Vector tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.