Hi Tip-Sheeters,

In Tip Sheet #22, I demonstrated creating an MCP server to connect to my Football API. This week, I'll update that demo to the latest version of FastMCP and MCP Cloud. It's another good chance to pitch you why you need an ongoing side project, which I'm calling an Anchor Project.

Why You Need an Anchor Project ⚓💪

I learn best by building, and I've found that a lot of other tech and data people do as well. A valuable method for me is to build real-world projects, as I wrote about on the ODSC blog: Three-Legged Side Projects: A Full Stack Data Scientist’s Secret Weapon.

If you keep the project going over time, it helps you to anchor your technical learning on a substantial use case that you understand and have deep expertise in. Let's call this your Anchor Project: a long-term project you use to test and learn new technologies with a consistent use case.

Benefits of an Anchor Project

Anchoring your learning to a consistent use case instead of using random example data has some benefits:

• You can be self-directed in your learning because you have a business goal to achieve, and you're interrogating the new tech for how it can solve it.
• You have domain expertise, so you can evaluate the capabilities of a new tech more thoroughly. (I call this the "sniff test".)

Finding Your Anchor

Take a look at some of the side projects you've worked on already -- is there one that you really enjoyed the dataset or the use case you built? Look across your projects, is there a recurring theme in them? Lean into that in your next project: that's your anchor.

FastMCP Update

In Tip Sheet #22, I implemented the Python quickstart from the MCP official page, and then modified it to use my SportsWorldCentral API (my anchor project). I didn't realize it at the time, but the Python code for that was built using an open-source project called FastMCP 1.0, which is maintained by Jeremiah Lowin.

In my demo project, I created a FastMCP server that accessed my API, installed it on my local machine, and called the server from Claude Desktop. The repo for that project is here: https://github.com/Ryandaydev/mcp_server_demo​

MCP has grown a lot since March, and that has led to two big advances that affect this project:

• FastMCP has released a 2.0 version.
• Jeremiah's company (Prefect) has launched an MCP cloud host called FastMCP Cloud.

This week, I updated the code to use FastMCP 2.0 and deployed it on FastMCP Cloud.

Updating the code had a couple of simple steps:

1. Change the import to `from fastmcp import FastMCP` (referenced here).
2. Adding a standard Python main entrypoint function (referenced here).

Jumping In

All of my updated code is in the new repo here: Ryandaydev/mcp_2_demo, and the MCP server file is still named `football_server.py`. Here are the changes:

I updated the imports to: `from fastmcp import FastMCP`

And I added the entrypoint method at the bottom of the file:

```

if __name__ == "__main__":
mcp.run()

```

I committed these changes, and I'm ready to deploy.

Deploying to FastMCP Cloud

On the FastMCP Cloud signup page, you'll be asked to setup an account with a GitHub account. The pricing page says Hobby accounts are free forever.

I created a new project and pointed it to my demo repository:

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I pointed it to the `football/football_server.py` file and started the deployment. Here's what it looks like once it is deployed successfully:

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After deploying, I went to Configuration and turned off authentication because I got an error when testing. (I probably could have done this during setup?) Now I was ready to test.

Claude Desktop Is a No-Go

Last time I demoed this, I used Claude Desktop to run the server code locally. Now I'd like to use Claude to work with my remote-hosted server, but unfortunately, this isn't available in the free version 🙁.

No fear, I can test the MCP server directly in FastMCP cloud. Click Inspector, select the `get_counts` tool.

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Now select Execute Tool and you'll see the results of the MCP server calling my API in the right-hand pane.

Closing Thoughts on FastMCP

The MCP space is moving fast, and security is an area that still needs some maturing. My demo didn't explore any authentication, because it's my public demo API.

Every indication I see is that MCP is going to be the de facto method of using data with LLMs for the near term, so it's worthwhile to jump in and explore.

If you have a chance to use FastMCP, let me know what you find!

Keep coding,

​

Ryan Day

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👉 https://tips.handsonapibook.com/ -- no spam, just a short email every week.

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