How to Use the Kibana MCP in LangChain

Q: How do I query Kibana data using a LangChain agent?

You integrate the MCP tools into your agent's toolset. Use findsavedobjects to locate specific dashboards, then chain that output into your prompt to retrieve details.

Q: Can LangChain handle Kibana bulk operations?

Yes. You pass bulkcreatesavedobjects or bulkupdatesavedobjects directly to your agent. It handles the list of objects as a single unit of work.

Chain your observability tasks in LangChain. Automate dashboard migrations and role updates without leaving your agent loop.

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Connect Kibana MCP to LangChain

Create your Vinkius account to connect Kibana 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 Kibana with LangChain

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Automate Kibana workflows in chains

Connect your MCP server to your LangChain agents to manage saved objects programmatically. Use `bulk_create_saved_objects` to deploy dashboards across environments as part of a single execution chain. Your agent handles the entire lifecycle. It triggers `create_data_view` to set up new indices, then moves objects where they need to go using `copy_saved_objects` without manual intervention.

Manage spaces and cases dynamically

Build multi-step logic that reacts to incident alerts by creating cases automatically. When an alert fires, your chain invokes `create_case` and attaches relevant context via `add_case_comment`. Keep your observability stack clean by using `list_spaces` to audit your environment before running clean-up tasks. You can delete outdated objects with `delete_saved_object` based on the output of your search queries.

Observe your agent's API performance

Trace every tool call inside LangSmith to see how your agent interacts with the Kibana API. You'll see exactly which parameters were sent to `update_rule` and how long the server took to respond. This visibility prevents issues with malformed requests. If a `bulk_update_saved_objects` call fails, the logs show you the exact error response, letting you adjust your agent's logic before the next run.

Setup guide

Set up Kibana 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 Kibana 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({
    "kibana-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 Kibana transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Kibana MCP in LangChain

You integrate the MCP tools into your agent's toolset. Use `find_saved_objects` to locate specific dashboards, then chain that output into your prompt to retrieve details.

Yes. You pass `bulk_create_saved_objects` or `bulk_update_saved_objects` directly to your agent. It handles the list of objects as a single unit of work.

The server remains stateless, but your LangChain agent manages context. You can use the `client.session()` method to maintain state across multiple interactions.

LangChain raises an exception for the failed call. You can catch this in your agent logic and use `resolve_import_errors` to fix issues.

Only the tools you explicitly provide have access to your instance. Your data remains on your infrastructure, and only the metadata defined in the schema is transmitted.

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