Kibana MCP
This project is an implementation of a Kibana MCP server that allows AI assistants to interact with Kibana security features, including alert, rule, and exception management, through the Model Context Protocol (MCP).
rating : 2.5 points
downloads : 15
🚀 Kibana-MCP Server Documentation
This documentation provides a comprehensive guide on installing dependencies, building, publishing, and setting up a development and testing environment for the Kibana-MCP server.
📦 Installation
Use uv
to synchronize dependencies:
uv sync
🚀 Build and Publish
Prepare a Release Version
-
Build the package distribution:
uv build
This will create source and wheel distributions in the
dist/
directory. -
Publish to PyPI:
uv publish
Note: You need to configure your PyPI credentials.
💻 Development Environment and Testing
Dependencies
Install development dependencies:
pip install -r requirements-dev.txt
Quick Start Script
Run the quick start script from the project root directory:
./testing/quickstart-test-env.sh
The script (testing/main.py
) will perform the following actions:
- Check for Docker and Docker Compose.
- Parse the
testing/docker-compose.yml
configuration. - Run
docker compose up -d
. - Wait for the Elasticsearch and Kibana APIs to start.
- Create a custom user (
kibana_system_user
) and role for internal Kibana use. - Create an index template (
mcp_auth_logs_template
). - Read
testing/sample_rule.json
(a detection rule) and send a POST request tohttp://localhost:5601/api/detection_engine/rules
to create the rule. - Write sample data from
testing/auth_events.ndjson
to themcp-auth-logs-default
index. - Check for detection signals at
http://localhost:5601/api/detection_engine/signals/search
. - Print the status, URL, credentials, and shutdown command.
Stop the Test Environment
- Run the shutdown command printed by the script (e.g.,
docker compose -f testing/docker-compose.yml down
). Use the-v
flag (down -v
) to remove data volumes.
Featured MCP Services

Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
140
4.5 points

Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
829
4.3 points

Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
1.7K
5 points

Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
86
4.3 points

Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
6.7K
4.5 points

Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
565
5 points

Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
282
4.5 points

Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
753
4.8 points