How to Use the Elastic Enterprise Search MCP in LangChain

Q: How do I perform a search query using Elastic Enterprise Search in LangChain?

You pass the search tool into your agent's toolkit. The agent then dynamically executes queries against your engines based on the input prompt.

Q: Can I index documents into Elastic Enterprise Search using LangChain directly?

Yes, you use the indexdocuments tool. It accepts JSON payloads that you can generate as part of your agent's reasoning process.

Q: Does Elastic Enterprise Search provide query analytics for LangChain agents?

It does. You use the analytics tool to pull metrics, which you can then pass through your chain for logging or decision-making.

Build custom search chains in LangChain by connecting your agents directly to Elastic Enterprise Search.

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Connect Elastic Enterprise Search MCP to LangChain

Create your Vinkius account to connect Elastic Enterprise Search to LangChain 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 LangChain

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Claude

Perplexity

Chainable indexing and search

Pipe document data directly into your chains using `index_documents`. You define the schema, and the agent handles the ingestion flow without extra boilerplate. Once indexed, the `search` tool lets your agent query your Elastic engines as a discrete step. The output feeds into your next chain link for immediate processing.

Real-time search insights

Hook the `analytics` tool into your LangSmith traces to monitor query performance in real-time. You'll see exactly how your search strategy performs across different sessions. This data helps you tune your search logic based on actual agent-user interactions. It’s a closed-loop system for improving your search relevance.

Dynamic engine administration

Use `list_engines` and `get_engine` to let your LangChain agents discover and configure search endpoints dynamically. The agent decides which engine to target based on the user's intent. This removes the need to hardcode engine IDs in your application logic. Your agents adapt to your search infrastructure as it grows.

Setup guide

Set up Elastic Enterprise Search MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Elastic Enterprise Search tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "elastic-enterprise-search-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Elastic Enterprise Search transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Elastic Enterprise Search MCP in LangChain

You pass the `search` tool into your agent's toolkit. The agent then dynamically executes queries against your engines based on the input prompt.

Yes, you use the `index_documents` tool. It accepts JSON payloads that you can generate as part of your agent's reasoning process.

It does. You use the `analytics` tool to pull metrics, which you can then pass through your chain for logging or decision-making.

We use an ephemeral sandbox for every request. Your credentials are never stored, and access is restricted to the specific endpoint token you provide.

The server doesn't keep your data. It acts as a bridge to your Elastic instance, and once the task is done, the connection is dropped.

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