About Rancher
Rancher is an MCP server for connecting GenAI applications to Rancher’s Kubernetes management and deployment API. It provides broad API coverage with multiple authentication methods and request handling features.
Key features
- 01Broad Rancher API coverage
- 02Multiple authentication methods
- 03Rate limiting
- 04Error handling and retries
- 05Asynchronous support
Use cases
- 01Connecting GenAI apps to Rancher
- 02Handling API requests with retries
MCP support
Exposes Rancher API capabilities to MCP clients, enabling GenAI applications to integrate with Rancher.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx rancher-mcp-serverRANCHER_API_KEYSecretAPI Key for RANCHERRANCHER_API_URLAPI URL for RANCHERLocal 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 READMERancher MCP Server
A Model Context Protocol (MCP) server for integrating Rancher with GenAI applications.
Overview
Kubernetes management and deployment platform
Features
- Comprehensive Rancher API coverage
- Multiple authentication methods
- Enterprise-ready with rate limiting
- Full error handling and retry logic
- Async support for better performance
Installation
pip install rancher-mcp-server
Or install from source:
git clone https://github.com/asklokesh/rancher-mcp-server.git
cd rancher-mcp-server
pip install -e .
Configuration
Create a .env file or set environment variables according to Rancher API requirements.
Quick Start
from rancher_mcp import RancherMCPServer
# Initialize the server
server = RancherMCPServer()
# 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.