K

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).
2.5 points
15
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

🚀 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

  1. Build the package distribution:

    uv build
    

    This will create source and wheel distributions in the dist/ directory.

  2. 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:

  1. Check for Docker and Docker Compose.
  2. Parse the testing/docker-compose.yml configuration.
  3. Run docker compose up -d.
  4. Wait for the Elasticsearch and Kibana APIs to start.
  5. Create a custom user (kibana_system_user) and role for internal Kibana use.
  6. Create an index template (mcp_auth_logs_template).
  7. Read testing/sample_rule.json (a detection rule) and send a POST request to http://localhost:5601/api/detection_engine/rules to create the rule.
  8. Write sample data from testing/auth_events.ndjson to the mcp-auth-logs-default index.
  9. Check for detection signals at http://localhost:5601/api/detection_engine/signals/search.
  10. 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.
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