About Rundeck
Rundeck is an MCP server that connects GenAI applications to Rundeck’s runbook automation and job scheduling API. It offers broad API coverage, multiple authentication methods, and request handling features.
Key features
- 01Broad Rundeck API coverage
- 02Multiple authentication methods
- 03Rate limiting
- 04Error handling and retries
- 05Asynchronous support
Use cases
- 01Connecting GenAI apps to Rundeck
- 02Handling API requests with retries
MCP support
Exposes Rundeck API capabilities to MCP clients, enabling GenAI applications to integrate with Rundeck.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx rundeck-mcp-serverRUNDECK_API_KEYSecretAPI Key for RUNDECKRUNDECK_API_URLAPI URL for RUNDECKLocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMERundeck MCP Server
A Model Context Protocol (MCP) server for integrating Rundeck with GenAI applications.
Overview
Runbook automation and job scheduling
Features
- Comprehensive Rundeck API coverage
- Multiple authentication methods
- Enterprise-ready with rate limiting
- Full error handling and retry logic
- Async support for better performance
Installation
pip install rundeck-mcp-server
Or install from source:
git clone https://github.com/asklokesh/rundeck-mcp-server.git
cd rundeck-mcp-server
pip install -e .
Configuration
Create a .env file or set environment variables according to Rundeck API requirements.
Quick Start
from rundeck_mcp import RundeckMCPServer
# Initialize the server
server = RundeckMCPServer()
# Start the server
server.start()
License
MIT License - see LICENSE file for details
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.