---
title: "Deploy FastMCP"
description: "Deploy and Host FastMCP with Railway"
category: "AI/ML"
url: https://railway.com/deploy/fastmcp
---

# Deploy FastMCP

Deploy and Host FastMCP with Railway

**[Deploy FastMCP on Railway](https://railway.com/template/fastmcp)**

- **Creator:** Mark Chen's Projects
- **Category:** AI/ML
- **Total deploys:** 42

## Template content

### FastMCP_template

- **Source:** yuting1214/FastMCP_template
- **Public domain:** Yes

## Documentation

# Deploy and Host FastMCP on Railway

FastMCP is a Python framework for building Model Context Protocol servers. It enables AI applications to safely access tools and resources through a standardized interface, making it easy to create scalable, production-ready MCP servers.

## About Hosting FastMCP

Hosting FastMCP on Railway provides a seamless way to deploy your Model Context Protocol servers to the cloud. FastMCP servers expose tools via HTTP endpoints that AI agents can call to perform tasks. 

## Common Use Cases

- AI agents accessing custom tools and data sources through a standardized interface
- Building tool services that integrate with Claude, ChatGPT, or other AI models
- Creating microservices that expose business logic as callable tools for AI applications
- Hosting multiple tool servers for specialized AI workflows

## Dependencies for FastMCP Hosting

- Python 3.12+
- FastMCP framework
- Docker (automatically handled by Railway)

### Deployment Dependencies

- [Railway Platform](https://railway.app) - Container hosting and deployment
- [FastMCP Documentation](https://gofastmcp.com) - Framework reference
- [uv Package Manager](https://astral.sh/uv/) - Python package management

### Implementation Details

**Server Setup (my_server.py):**
```python
from fastmcp import FastMCP

mcp = FastMCP("My MCP Server")

@mcp.tool
def greet(name: str) -> str:
    return f"Hello, {name}!"

async def main():
    await mcp.run_async()

if __name__ == "__main__":
    asyncio.run(main())
```

**Connect Remote Client (my_client_remote.py):**
```python
import asyncio
from fastmcp import Client

RAILWAY_URL = "https://your-railway-url.up.railway.app/mcp"
client = Client(RAILWAY_URL)

async def call_tool(name: str):
    async with client:
        result = await client.call_tool("greet", {"name": name})
        print(result)

if __name__ == "__main__":
    asyncio.run(call_tool("Ford"))
```

**Dockerfile:**
The provided Dockerfile automatically builds and runs your FastMCP server with:
- Python 3.12 base image with uv pre-installed
- Dependency installation using uv sync
- HTTP server listening on port 8080
- Automatic health checks and log streaming

## Why Deploy FastMCP on Railway?


Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying FastMCP on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.


**FastMCP-Specific Advantages:**

- **One-Click Deploy** - Connect your GitHub repo, Railway auto-detects the Dockerfile and deploys
- **Global HTTPS Endpoint** - Automatic SSL/TLS certificates, no manual configuration
- **AI-Ready** - Your tools instantly accessible to Claude and other AI models via standard MCP protocol
- **Auto-Deploy** - Push to GitHub, Railway redeploys automatically
- **Built-in Monitoring** - View logs, performance metrics, and errors in real-time
- **Custom Domains** - Add your own domain for professional tool services

**Getting Started:**

1. Push this repository to GitHub
2. Go to [railway.app](https://railway.app)
3. Create new project → "Deploy from GitHub repo"
4. Select this repository
5. Railway deploys automatically with your public HTTPS URL

Your FastMCP server is now live and ready to serve as a tool provider for AI applications!


## Similar templates

- [Chat Chat](https://railway.com/deploy/-WWW5r) — Chat Chat, your own unified chat and search to AI platform.
- [stella](https://railway.com/deploy/stella) — Self-host stella with web, API, Postgres, Redis, and object storage.
- [Hermes Agent | OpenClaw Alternative with Dashboard](https://railway.com/deploy/hermes-agent-or-openclaw-alternative-wit) — Self-Hosted Hermes AI Agent for Telegram, Discord & Slack

Open this page in a browser: https://railway.com/deploy/fastmcp