"Compare all differences between two source code folders, including binary files" matching MCP connectors:

Give your AI assistant access to real Helm chart data. No more hallucinated values.yaml files.

Deploy files, sites, and Dockerfile apps to live URLs + private drives for agent memory.

Zero-Ops deploy of a private AI workspace to your own VPS — from your AI chat. Free and open-source.

The Google Compute Engine MCP server is a fully-managed Model Context Protocol server that provides tools to manage Google Compute Engine resources through AI agents. It enables capabilities including instance management (creating, starting, stopping, resetting, listing), disk management, handling instance templates and group managers, viewing machine and accelerator types, managing images, and accessing reservation and commitment information. The server operates as a zero-deployment, enterprise-grade endpoint at https://compute.googleapis.com/mcp with built-in IAM-based security.

The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.

Validates AI infra code on real VMs. Self-corrects until it works. No containers, no sandboxes.

Deploy containers on Kubernetes with x402 billing. 9 workload types and source builds.

Deploy and manage cloud servers from AI agents. Create pods, push code, run commands — all via MCP.