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 LangChain 6 tools — connect in under 2 minutes

Name: Elasticsearch Vector
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Elasticsearch Vector through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "elasticsearch-vector": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Elasticsearch Vector, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

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.

LangChain's ecosystem of 500+ components combines seamlessly with Elasticsearch Vector through native MCP adapters. Connect 6 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from Elasticsearch Vector via MCP

Why Use LangChain with the Elasticsearch Vector MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine Elasticsearch Vector MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Elasticsearch Vector queries for multi-turn workflows

Elasticsearch Vector + LangChain Use Cases

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

RAG with live data: combine Elasticsearch Vector tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Elasticsearch Vector, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Elasticsearch Vector tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Elasticsearch Vector tool call, measure latency, and optimize your agent's performance

Elasticsearch Vector MCP Tools for LangChain (6)

These 6 tools become available when you connect Elasticsearch Vector to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Elasticsearch Vector + LangChain FAQ

Common questions about integrating Elasticsearch Vector MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.