Kibana MCP Server

Overview

The Kibana MCP Server bridges the gap between AI assistants and Elastic’s Kibana dashboards by exposing every Kibana API endpoint through the Model Context Protocol (MCP). This enables developers to query, modify, and visualize Kibana data directly from natural‑language interfaces such as Claude Desktop or any other MCP‑compatible client. By abstracting the intricacies of Kibana’s RESTful API, the server lets AI agents perform complex operations—like creating visualizations, managing spaces, or retrieving saved searches—without the user having to write HTTP requests or understand Kibana’s authentication flows.

Problem Solved

Working with Kibana programmatically can be tedious: developers must navigate a sprawling API, handle authentication (basic or cookie‑based), and translate JSON payloads into actionable commands. When building AI‑powered tools, this friction hampers rapid iteration and user experience. The Kibana MCP Server removes these barriers by automatically loading the OpenAPI specification from Elastic Stack 8.x, generating a comprehensive MCP resource catalog, and handling authentication transparently. As a result, AI assistants can issue high‑level commands like “Show me the latest error logs” or “Create a new dashboard in space devops,” and the server translates them into precise Kibana API calls.

Core Value for Developers

• Seamless AI Integration: Plug the server into Claude Desktop or any MCP client with a single configuration entry. The AI can now interrogate Kibana dashboards, modify visualizations, and manage spaces using plain language or structured prompts.
• Dynamic API Exposure: The server pulls the latest OpenAPI YAML from Elastic, ensuring that new endpoints or changes in the API are automatically reflected without manual updates.
• Flexible Authentication: Support for both basic and cookie‑based authentication allows developers to use existing Kibana credentials or session cookies, simplifying deployment in varied environments.
• Standalone HTTP Mode: For teams that prefer a dedicated service, the server can run as an HTTP endpoint on any host and port, making it easy to integrate with CI/CD pipelines or other microservices.

Key Features

• Full Kibana API Coverage: Every endpoint from Elastic’s official documentation is available, including spaces, dashboards, visualizations, and saved objects.
• Automatic Resource Discovery: The server introspects the OpenAPI spec to expose resources, tools, and prompts that MCP clients can consume.
• Environment‑Based Configuration: All settings (URL, credentials, default space) are passed through environment variables or a file, keeping secrets out of code.
• HTTPS/HTTP Transport Options: The flag allows the server to expose a RESTful interface for remote clients, expanding its use beyond local CLI or desktop integration.
• Security‑First Design: The project includes a security assessment badge and encourages the use of TLS verification settings, ensuring that sensitive data remains protected.

Real‑World Use Cases

• Automated Incident Response: An AI assistant can pull the latest alerts from Kibana, correlate them with external data sources, and even create new dashboards to visualize the incident timeline.
• DevOps Dashboards on Demand: Developers can ask for a “performance dashboard for service X” and the AI will create or update the Kibana space, fetch relevant metrics, and present them in a ready‑to‑use format.
• Data Exploration: Analysts can query Kibana’s saved searches or visualizations using natural language, accelerating the data discovery process without leaving their AI environment.
• Continuous Integration: CI pipelines can invoke the MCP server to validate Kibana configurations, run health checks, or generate automated reports before deployment.

Standout Advantages

Unlike generic HTTP wrappers, the Kibana MCP Server is specification‑driven, meaning it automatically adapts to changes in Elastic’s API without manual intervention. Its tight integration with MCP tools ensures that AI assistants can not only retrieve data but also act on it—creating, updating, and deleting resources—all through conversational commands. This level of automation transforms Kibana from a static visualization platform into an interactive, AI‑powered data engine that scales with your organization’s needs.