Discover our curated collection of MCP servers for deployment & devops. Browse 3069 servers and find the perfect MCPs for your needs.
Enables querying and listing Azure resources and costs directly from an MCP client.
Generates release notes from GitHub commits, organized by type and enriched with PR data.
Enables AI tools to interact with Kubernetes clusters using natural language, facilitating advanced automation and interaction.
Integrates n8n workflow automation with Claude Code and Model Context Protocol (MCP) servers to enable AI-powered project management, automated memory dumps, and intelligent development workflows.
Enables AI-assisted, human-in-the-loop screen interaction capabilities through a streamlined, containerized infrastructure.
Provides a robust backend playground for building and experimenting with a Todo API using NestJS, MongoDB, and Redis, complete with authentication, DTO validation, and Docker support.
Orchestrates Docker containers, stacks, and hosts across your infrastructure using AI assistant commands.
Intelligently manages Model Context Protocol servers for Claude, dynamically loading tools on-demand to significantly reduce token usage and optimize context windows.
Integrates the Xray Cloud GraphQL API with Model Context Protocol (MCP) clients like Claude Code.
Manages Tailscale networks with a FastMCP 2.12 compliant server, offering extensive control and observability.
Empower AI assistants to manage, monitor, and secure cloud resources across AWS, Azure, and GCP through the Model Context Protocol.
Provides comprehensive filesystem operations accessible via the Streamable HTTP Model Context Protocol.
Manages remote Linux servers through an AI assistant, enabling command execution, file transfers, and automation via a natural chat interface.
Provides an agent-friendly API for managing and orchestrating AFL++ fuzzing campaigns and security testing workflows.
Centralizes Model Context Protocol (MCP) server management, routing client requests, and enforcing fine-grained access control across multiple backend servers through a unified interface.
Schedules HTTP jobs that adapt their frequency and actions in real time based on AI analysis of response data.
Provides core tool implementations and shared helper code for the PlanetScale Model Context Protocol server.
Provides a sandboxed Linux container runtime for AI agents, integrating an embedded Model Context Protocol (MCP) resolver.
Prevents technical debt and security vulnerabilities by performing AI-powered pre-commit code quality analysis with ML semantic capabilities across 21 languages and formats.
Empower AI agents to manage remote servers by interacting with local tmux sessions, especially in complex jump-host environments.
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