Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AtlasOS create a workflow to research competitors and summarize findings"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AtlasOS is an intelligent orchestration control plane that transforms natural-language objectives into executable workflows using the Model Context Protocol…
AtlasOS — The AI Operating System for Enterprise Orchestration is an MCP (Model Context Protocol) server that extends AI assistants — like Claude, Cursor, and any MCP-compatible client — with new, real-world capabilities. It is built and deployed on Nitrostack, the fastest way to build, deploy, and share MCP apps.
Related MCP server: workflows-mcp
AtlasOS is an intelligent orchestration control plane that transforms natural-language objectives into executable workflows using the Model Context Protocol (MCP). Instead of acting as a conventional chatbot, AtlasOS plans, coordinates, executes, and synthesizes complex multi-step tasks across connected AI agents, enterprise tools, and external services.
A single prompt is automatically decomposed into a structured execution graph, where each node represents a specialized capability such as web research, code generation, database operations, file analysis, API interaction, or business intelligence. AtlasOS dynamically selects the appropriate MCP capabilities, executes them in real time, streams live execution updates, gracefully handles failures through resilient fallback mechanisms, and combines the outputs into a coherent, actionable response.
Designed with a production-grade architecture, AtlasOS features a FastAPI backend, real-time WebSocket synchronization, dynamic workflow visualization, capability discovery, execution monitoring, and fault-tolerant orchestration. Every workflow is generated dynamically—there are no hardcoded execution paths or static responses—providing complete transparency into how AI systems reason, plan, and execute.
Whether automating enterprise operations, accelerating software development, coordinating cybersecurity investigations, or powering intelligent business workflows, AtlasOS serves as the operating system that connects people, AI models, and tools into a unified execution platform.
Key Features 🧠 Natural Language → Executable Workflows 🔗 MCP-Native Tool & Agent Orchestration 📊 Dynamic Workflow Graph Visualization ⚡ Real-Time Execution Monitoring via WebSockets 🛡️ Fault-Tolerant Execution with Intelligent Recovery 🔍 Transparent AI Planning & Decision Making 🔌 Extensible Plugin-Based Capability Registry 🏢 Enterprise-Ready Architecture
The Model Context Protocol (MCP) is an open standard that lets AI assistants securely connect to external tools, data sources, and services. Instead of being limited to what it was trained on, an AI model can call MCP servers to fetch live data, run actions, and integrate with real systems.
This project is one such MCP server. Learn more about building and shipping MCP apps at nitrostack.ai.
🔌 MCP-native — works with any MCP-compatible client (Claude, Cursor, and more)
🛠️ Tools, resources & prompts — exposes structured capabilities to AI agents
⚡ Deployed on Nitrostack — reliable, hosted, and instantly shareable
🔐 Secure by design — secrets stay in environment variables, never in code
🧩 Composable — combine with other MCP apps to build powerful AI workflows
🚀 Live MCP endpoint: https://atlas0s-6a5a9bad-codesmiths-amrita-university-amritapuri-campus.app.nitrocloud.ai
Point your MCP client at the endpoint above to try it instantly. Prefer a hosted setup? Deploy your own in minutes on Nitrostack.
Node.js 18+ (or your project runtime)
An MCP-compatible client (Claude Desktop, Cursor, etc.)
git clone https://github.com/your-username/your-mcp-project.git
cd atlasos-the-ai-operating-system-for-enterprise-orchestration
npm installCopy the example environment file and add your own values:
cp .env.example .envnpm run startAdd this server to your MCP client configuration. A typical entry looks like:
{
"mcpServers": {
"atlasos-the-ai-operating-system-for-enterprise-orchestration": {
"url": "https://atlas0s-6a5a9bad-codesmiths-amrita-university-amritapuri-campus.app.nitrocloud.ai"
}
}
}Restart your client and the tools from this MCP server will be available to your AI assistant.
Want to build and ship an MCP server like this one? Nitrostack lets you create, deploy, and host MCP apps in minutes — no infrastructure to manage.
👉 Start building: https://nitrostack.ai
🌙 Discover and share MCP projects with the community on r/mcptothemoon
🧰 Browse a growing catalog of MCP apps on Nitrostack
An MCP server implements the Model Context Protocol to expose tools, resources, and prompts that AI assistants can call. It lets an AI model take real actions and access live data.
AtlasOS is an intelligent orchestration control plane that transforms natural-language objectives into executable workflows using the Model Context Protocol…
Any MCP-compatible client, including Claude Desktop and Cursor. New clients are adding MCP support regularly.
Use Nitrostack to build, deploy, and host MCP apps without managing infrastructure.
Enterprise AI & Workplace Automation · AtlasOS — The AI Operating System for Enterprise Orchestration · MCP · Model Context Protocol · MCP server · MCP app · AI tools · AI agents · LLM tools · Claude MCP · Nitrostack · deploy MCP server · build MCP app
MIT © 2026
Built with ❤️ using the Model Context Protocol on Nitrostack. Share your MCP app on r/mcptothemoon.
Perform basic arithmetic calculations
Inputs: a (required), b (required), operation (required).
Convert temperature units based on file content or direct input. Supports Celsius (C) and Fahrenheit (F).
Inputs: file_name (required), file_type (required), file_content (required), value, +2 more.
Execute a single step from an execution plan with resilience and fallback handling
Inputs: plan_id (required), action, step_id, capability_name, +1 more.
Generate a structured execution plan from a natural language goal
Inputs: goal (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 4 tools
Each tool targets a distinct operation: arithmetic, temperature conversion, plan generation, and step execution. No overlap in functionality.
Naming style is inconsistent: 'calculate' and 'convert_temperature' use snake_case, while 'generate-plan' and 'execute-step' use hyphens. All are imperative verbs but the format varies.
Four tools is on the low end but still reasonable for a focused utility server. Each tool serves a clear purpose without redundancy.
Obvious gaps exist: no Kelvin in temperature conversion, no way to list or delete plans beyond generate/execute. Lacks full CRUD for plan management.
AI work orchestration for plans, tasks, teams, and coding-agent dispatch.
Context and control for any AI. Agent identity, scoped access, and proof across tools.
Task manager for AI agents. Orchestrate agents to achieve large goals in unified control plane.
Goal and task planning MCP for Codex and AI agents, with evidence-backed completion.
