The topic of Weather MCP is currently the subject of lively debate — readers and analysts are keeping a close eye on developments.
This is taking place in a dynamic environment: companies’ decisions and competitors’ reactions can quickly change the picture.
During my career break that I started in 2025, I started exploring the ever-changing Generative AI landscape. In Decemember, I was exploring Spring Boot as I hadn’t used it in anger professionally having used other Java frameworks like Jersey, Play and I wanted to build something.
In February, everyone in the UK was talking about how it had rained everyday in 2026 so far. I thought that generative AI chatbots like our friend ChatGPT shouldn’t miss out on the conversation.
So I set out to build a Model Context Protocol (MCP) server using Spring AI and developed using Cursor agents.
3 things I could tick off my tech backlog (which is probably just as long as my gaming backlog).
An Accuweather MCP server that provides prompts and tools for agents to be able to find out for a location:
I had fun building this out and thought I would wrap it up by creating this post, sharing the resources I used to learn as well as gotchas I encountered. Hopefully not only useful for future Jonathan, but others too 😀
You will see a pattern here, but it’s how I prefer to learn (I appreciate others may prefer to watch paint dry)
Cursor themselves provide great resources that includes plenty of guidance, recommendations and best practises.
If you want to get started and get to grips with the Model Context Protocol, the official documentation is great.
I even encourage anyone to read the specification as for servers it details error handling, implementation considerations and security.
The Spring AI reference provides plenty of guidance and recommendations around MCP as well as other parts of Genative AI such as using chat clients or RAG.
One especially helpful resource were the Spring AI examples that showcase putting everything together. for example, how to use the MCP annotations to create MCP tools.
Time for something more interesting, below are things that I encountered whilst building out my MCP server that took a minute to resolve and more likely help the next person.
My goal of this side project was to get used to Agentic development and minimise hand-rolling code. When I started out with a blank slate and aimed to build the current weather feature, I tried one-shotting it by generating a plan and seeing what the result was. Although it did the job, the Agent produced a lot of code to review and used patterns that I didn’t want to use. So I then had to course correct with more prompts.
A lot of this is in the Cursor docs, but here’s how I improved things for the next feature:
By doing all of this, when it came to the hourly forecast feature, I delegated the entire thing to a Cloud Agent and only had to review the changes.
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Why it matters
News like this often changes audience expectations and competitors’ plans.
When one player makes a move, others usually react — it is worth reading the event in context.
What to look out for next
The full picture will become clear in time, but the headline already shows the dynamics of the industry.
Further statements and user reactions will add to the story.