Awesome MCP DevOps
A curated, DevOps-focused list of Model Context Protocol (MCP) servers—covering source control, IaC, Kubernetes, CI/CD, cloud, observability, security, and collaboration—with a bias toward maintained, production-ready integrations.
What is Awesome MCP DevOps?
Awesome MCP DevOps is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to curated, devops-focused list of model context protocol (mcp) servers—covering source control, iac, kubernetes, ci/cd, cloud, observability, security, and collaboration—with a bias toward maintained, p...
A curated, DevOps-focused list of Model Context Protocol (MCP) servers—covering source control, IaC, Kubernetes, CI/CD, cloud, observability, security, and collaboration—with a bias toward maintained, production-ready integrations.
This server falls under the Monitoring & Observability category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- A curated, DevOps-focused list of Model Context Protocol (MC
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx awesome-mcp-servers-devopsConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Awesome MCP DevOps
Awesome MCP Servers for DevOps is a curated reference list maintained by the Wagner AI DevOps platform, cataloguing production-ready MCP server integrations across 14 DevOps domains: source control (GitHub, GitLab, Azure DevOps), Infrastructure as Code (Terraform, Pulumi, Vault), Kubernetes and containers, CI/CD (Argo CD, Jenkins, GitHub Actions), cloud platforms (AWS, Azure, Cloudflare), observability (Grafana, Datadog, Prometheus), security scanning, and team collaboration tools. It serves as a structured starting point for engineers building AI-assisted DevOps workflows.
Prerequisites
- Familiarity with Model Context Protocol (MCP) concepts — servers, clients, and tools
- An MCP client installed: Claude Desktop, Cursor, Cline, or VS Code with Copilot
- Access credentials for whichever DevOps platforms you want to connect (e.g. GitHub token, AWS credentials, Datadog API key)
- Node.js or Python runtime depending on the specific MCP server you choose from the list
Browse the curated list on GitHub
Visit the repository to explore the 14 categories and identify which MCP servers are relevant to your DevOps stack. Each entry links to the server's own repository with specific install instructions.
Choose a category and pick a server
Identify the DevOps tool you want to connect your AI to — for example, Kubernetes for cluster management, Terraform for IaC, or Grafana for observability. Find the corresponding MCP server entry in the list.
Follow the individual server's installation instructions
Each listed server has its own README with install steps. The typical pattern is an npx command or uvx command that requires an API key or token for the target platform.
# Example pattern for most npm-based servers in the list:
npx -y <server-package-name>Apply the recommended security practices
The list advises starting with read-only API tokens, scoping tokens to only the required permissions, testing in a staging environment before production, and auditing all AI-initiated actions.
Add your chosen server to your MCP client config
Use the standard MCP client configuration pattern. Replace the command, args, and env values with those from the specific server's documentation.
{
"mcpServers": {
"your-devops-tool": {
"command": "npx",
"args": ["-y", "<server-package>"],
"env": {
"API_TOKEN": "your-scoped-read-only-token"
}
}
}
}Awesome MCP DevOps Examples
Client configuration
Example configuration pattern used by servers in the awesome-mcp-servers-devops list, showing a typical npm-based DevOps MCP server setup.
{
"mcpServers": {
"your-devops-tool": {
"command": "npx",
"args": ["-y", "<mcp-server-package>"],
"env": {
"API_TOKEN": "your-scoped-read-only-token",
"BASE_URL": "https://your-service.example.com"
}
}
}
}Prompts to try
Once you have connected a DevOps MCP server from the list, typical AI assistant workflows include:
- "List the last 5 deployments in my Kubernetes cluster and their status"
- "Show me any Terraform plan drift between my state file and actual AWS infrastructure"
- "Summarise the last 24 hours of alerts from Grafana for the production environment"
- "Find any critical vulnerabilities in my container images using the Snyk MCP server"
- "What GitHub Actions workflows failed in the last hour and what were the error messages?"Troubleshooting Awesome MCP DevOps
Not sure which server to choose from the list
The list is organised by category (Source Control, IaC, Kubernetes, CI/CD, Cloud, Observability, Security, Collaboration). Start with the platform you use most and look for entries marked as 'official' (from the vendor) or 'maintained' rather than archived community forks.
A listed server has become unmaintained or its repository is archived
Check the GitHub repository's last commit date and open issues before adopting a server. The awesome-mcp-servers-devops list has a bias toward maintained, production-ready integrations, but always verify freshness before relying on a server in production.
AI actions on DevOps tools are causing unintended changes
Follow the list's safety guidance: use read-only API tokens where possible, scope tokens to minimum permissions, and test in a staging environment first. For tools that must write (deployments, runbooks), add an approval step in your workflow.
Frequently Asked Questions about Awesome MCP DevOps
What is Awesome MCP DevOps?
Awesome MCP DevOps is a Model Context Protocol (MCP) server that curated, devops-focused list of model context protocol (mcp) servers—covering source control, iac, kubernetes, ci/cd, cloud, observability, security, and collaboration—with a bias toward maintained, production-ready integrations. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Awesome MCP DevOps?
Follow the installation instructions on the Awesome MCP DevOps GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Awesome MCP DevOps?
Awesome MCP DevOps works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Awesome MCP DevOps free to use?
Yes, Awesome MCP DevOps is open source and available under the CC0-1.0 license. You can use it freely in both personal and commercial projects.
Awesome MCP DevOps Alternatives — Similar Monitoring & Observability Servers
Looking for alternatives to Awesome MCP DevOps? Here are other popular monitoring & observability servers you can use with Claude, Cursor, and VS Code.
Netdata
★ 78.9kReal-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
Kubeshark
★ 11.9keBPF-powered network observability for Kubernetes. Indexes L4/L7 traffic with full K8s context, decrypts TLS without keys. Queryable by AI agents via MCP and humans via dashboard.
Mission Control
★ 4.9kSelf-hosted AI agent orchestration platform: dispatch tasks, run multi-agent workflows, monitor spend, and govern operations from one mission control dashboard.
Grafana
★ 3.0kThis MCP server enables natural-language querying of Grafana logs by automatically detecting log sources and service labels. It provides read-only access to log data with intelligent caching for efficient repeat queries.
Sentrux
★ 2.4kReal-time architectural sensor that helps AI agents close the feedback loop, enabling recursive self-improvement of code quality. Pure Rust.
OpenInference
★ 986OpenTelemetry Instrumentation for AI Observability
Browse More Monitoring & Observability MCP Servers
Explore all monitoring & observability servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.
Set Up Awesome MCP DevOps in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
Ready to use Awesome MCP DevOps?
Browse our complete directory of 33,000+ MCP servers, read setup guides for your editor, and start building with the Model Context Protocol.