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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.
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
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.
Puter MCP lets your AI tools (Claude Code, Codex, or any other MCP-compatible client) interact with your Puter resources: managing files, publishing websites, deploying workers, and more.
Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more
Backend for AI-built apps: database, auth, files, email, AI, payments, deploy, realtime. 170+ tools.
Talk to your LLM and get a live web app deployed to a real URL.** onvibe.run is a conversational PaaS: you describe the app you want, the LLM builds it through MCP tools, and it ships to a public URL like `https://your-project.onvibe.run` — no dashboards, no config files, no manual deploys.