How to Deploy MCP Servers
Setting up a Model Context Protocol (MCP) server is easier than you might think! It is a straightforward process that doesn't require extensive DevOps skills. Whether you're testing, packaging, or scaling globally, the HAPI MCP Stack equips you with everything you need to launch your server in just seconds.
In this guide, we'll explore 4 different ways to run and deploy MCP servers using the HAPI CLI.
Bonus: Agent Skills provided to automate MCP server deployment.
1. Local Development (The Quickest Way)
For rapid iteration and testing, running your MCP server locally is the best approach. You can instantly spin up a server and inspect its tools using the official MCP Inspector.
In this example, we'll run an OpenAI Tools server.
# Run the MCP Server Locally and pipe to Inspector
hapi serve openai-tools \
--headless \
--port 3030 \
--url https://api.openai.com/v1 | bunx @modelcontextprotocol/inspector
Find more MCP server definitions in the official MCP Registry.
What's happening here?
hapi serve: Starts the MCP server.--headless: Runs without a UI (optimized for machine interaction).--port 3030: Binds the server to port 3030.| bunx @modelcontextprotocol/inspector: Pipes the output directly to the MCP Inspector, allowing you to interact with your tools in a web interface locally.
2. Docker Containers (The Portable Way)
If you need to ship your server to Kubernetes, ECS, or any container orchestration platform, Docker is the industry standard. We provide a pre-built image hapimcp/hapi-cli that is ready to serve.
docker run --name hapi-openai -d \
-p 3030:3030 \
hapimcp/hapi-cli:latest serve \
--openapi https://docs.mcp.com.ai/servers-apis/openapi/openai-tools.yaml \
--headless \
--url https://api.openai.com/v1
Breakdown:
hapimcp/hapi-cli:latest: The official HAPI CLI Docker image.--openapi ...: Specifies the OpenAPI definition for the server (defining the tools/resources).-p 3030:3030: Maps the container port to your host.
Your server is now running in a container, ready to accept connections on port 3030!
3. Cloudflare Workers (The Scalable Way)
For a serverless solution with global low latency, Cloudflare Workers is the ultimate deployment target. HAPI CLI handles the entire build and deployment process for you.
hapi deploy --name openai-tools \
--project openai \
--openapi https://docs.mcp.com.ai/servers-apis/openapi/openai-tools.yaml \
--url https://api.openai.com/v1
You'll see output like this:
📝 Generating temporary Wrangler config...
⚙️ Deploying to Cloudflare Workers...
🌍 Live at: https://openai-tools.runmcp.workers.dev
✅ Deployed successfully!
Why Cloudflare Workers?
- Zero Cold Starts: Instant response times.
- Global Distribution: Deployed to 300+ cities worldwide.
- Cost Effective: Pay only for what you use.
4. Fly.io (The Global Way)
For those who prefer running on Fly.io, you can deploy your MCP server instantly using our pre-built image, bypassing the need for Dockerfiles or complex overrides.
fly machine run hapimcp/hapi-cli:latest --command "hapi serve" \
-e openapi="https://docs.mcp.com.ai/servers-apis/openapi/openai-tools.yaml" \
-e url="https://api.openai.com/v1"
Why Fly.io?
- Instant Machines: Boot full VM instances in milliseconds.
- Global Anycast: Run close to your users.
- Pre-built Image: No build step required—just run.
Agent Skills to Automate Deployment
To make deployment even easier, we provide Agent Skills that automate the entire process of deploying MCP servers across different platforms. You can find this and more skills in the mcp.com.ai skills registry.
npx skills add https://github.com/mcp-com-ai/api-to-mcp-skills --skill api-to-mcp
Summary
| Method | Best For | Requirement |
|---|---|---|
| Local | Development, Testing, Debugging | HAPI CLI |
| Docker | Enterprise, Kubernetes, VPS | Docker Engine |
| Workers | Serverless, Scaling, Public APIs | Cloudflare Account |
| Fly.io | Global VMs, Persistent Apps | Fly.io Account |
Ready to build your own? Check out the full documentation at docs.mcp.com.ai.
🏁 Done. Be HAPI, and go Rebels! ✊