"Reading Microsoft OneDrive 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.

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.

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.

Deploy and manage web apps on InstaPods: create pods, push files, run commands, read logs.

Backend for AI-built apps: database, auth, files, email, AI, payments, deploy, realtime. 170+ tools.

Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more

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.