Is Nexlayer open source?
No. Nexlayer is a managed platform. This repository contains documentation, examples, and community resources—not the platform source code.
@Nexlayer
Basic information
Cloud infrastructure for AI agents.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"nexlayer": {
"command": "npx",
"args": [
"-y",
"nexlayer-mcp"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
Fetch the live tool list by running this server in a temporary sandbox using the button above.
Nexlayer Mcp is a managed cloud platform that lets AI agents deploy, scale, and manage applications autonomously. It is built on the Model Context Protocol (MCP) and allows developers to describe what they want in plain language; the platform handles infrastructure provisioning, networking, scaling, and monitoring. It is designed for anyone who wants to ship software without DevOps overhead.
Connect Nexlayer to your AI coding assistant by running npx @nexlayer/mcp-install. Then tell your assistant “Deploy this to Nexlayer”. That single command triggers the platform to configure, provision, and deploy your application.
No. Nexlayer is a managed platform. This repository contains documentation, examples, and community resources—not the platform source code.
Apps pause, data persists. You receive an email and have 30 days to export or resume. Nothing is deleted without explicit confirmation.
Currently, Nexlayer runs on its managed infrastructure. Bring‑your‑own‑cloud options are on the roadmap.
Those platforms require you to configure deployments. Nexlayer agents figure out the configuration—you describe intent; agents handle implementation.
Yes. The Nexlayer Agent Protocol is built on the Model Context Protocol (MCP), an open standard created by Anthropic, extended with Nexlayer’s embedded agent layer.
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Hindsight: Agent Memory That Learns

Yuppsy MCP: build and publish real estate websites from your AI assistant. Yuppsy lets your AI assistant create live websites for real estate agents and individual property listings. You don't need a designer or any code

A good sequence diagram can solve almost any communication problem in engineering. Volley lets your agent write them as plain text, with the real request and response payloads on every arrow. It checks its own syntax wi
Comments