Kibana MCP Server Integration

What does “Kibana” MCP Server do?

The Kibana MCP (Model Context Protocol) Server acts as a bridge connecting AI assistants and clients with Kibana, allowing for enhanced search, management, and automation within Kibana environments. By exposing Kibana’s functionalities through the MCP standard, this server enables AI-powered workflows to interact with Kibana resources—such as querying data, managing dashboards, or automating common tasks. This integration streamlines development workflows, supports data-driven decision-making, and empowers developers to build smarter tools by leveraging Kibana’s capabilities through standardized APIs and protocols.

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List of Resources

No explicit list of MCP resources provided in the available documentation or code.

List of Tools

No explicit tool definitions found in the available documentation or code. The repository may expose Kibana functionalities as tools, but these are not enumerated.

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Use Cases of this MCP Server

• Kibana Data Search Automation: Integrate AI assistants to perform automated searches and data queries within Kibana, reducing manual effort and enabling faster insights.
• Dashboard Management: Use the MCP interface to programmatically create, update, or manage Kibana dashboards, supporting CI/CD and DevOps workflows.
• Alert Monitoring: AI agents can access and monitor alerts or logs in Kibana, enabling proactive incident detection and resolution.
• Reporting and Visualization: Automate the generation and retrieval of visual reports from Kibana, integrating them into broader analytics pipelines.
• Access Control Automation: Leverage the MCP server to script and automate access control and user management in Kibana, enhancing security and compliance.

How to set it up

Windsurf

1. Ensure you have Node.js installed.
2. Locate the Windsurf configuration file (usually windsurf.config.json).
3. Add the Kibana MCP Server to the mcpServers section:

   {
     "mcpServers": {
       "kibana": {
         "command": "npx",
         "args": ["@tocharian/mcp-server-kibana@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify Kibana MCP Server is running in the Windsurf environment.

Claude

1. Ensure prerequisite dependencies (e.g., Node.js) are available.
2. Edit the Claude configuration file.
3. Add Kibana MCP Server as follows:

   {
     "mcpServers": {
       "kibana": {
         "command": "npx",
         "args": ["@tocharian/mcp-server-kibana@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm the MCP server is accessible.

Cursor

1. Install Node.js if not already present.
2. Open Cursor’s configuration.
3. Insert the following snippet into the mcpServers section:

   {
     "mcpServers": {
       "kibana": {
         "command": "npx",
         "args": ["@tocharian/mcp-server-kibana@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Check that Cursor connects to the Kibana MCP Server.

Cline

1. Make sure Node.js is installed on your system.
2. Update the Cline configuration file.
3. Add the Kibana MCP Server entry:

   {
     "mcpServers": {
       "kibana": {
         "command": "npx",
         "args": ["@tocharian/mcp-server-kibana@latest"]
       }
     }
   }
   

4. Save and restart Cline.
5. Confirm service availability.

Securing API Keys

Store your Kibana or Elasticsearch API keys using environment variables to enhance security. Example configuration:

{
  "mcpServers": {
    "kibana": {
      "command": "npx",
      "args": ["@tocharian/mcp-server-kibana@latest"],
      "env": {
        "KIBANA_API_KEY": "${KIBANA_API_KEY}"
      },
      "inputs": {
        "kibana_url": "https://your-kibana.example.com"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "kibana": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “kibana” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Roots support: Not documented
Sampling support: Not documented

Based on the information available, Kibana MCP Server provides a basic overview and setup documentation, with clear licensing and basic usage details but lacks documentation on prompts, resources, tools, and advanced MCP features. I would rate this MCP server a 4/10 for overall documentation and developer readiness.

MCP Score