Along with fellow panelists Jason Haley, Veronika Kolesnikova (the three of us run Boston Azure AI), and Udai Ramachandrans (he runs Nashua Cloud .NET & DevBoston), I was part of a Boston Azure AI event to discuss highlights from Microsoft’s 2025 Build conference. I knew a couple of the things I wanted to show off were GitHub Copilot Agent mode and hosting Model Context Protocol (MCP) tools in Azure Functions.
What I didn’t realize at first was that these would be the same demo.
I started with a solid sample C#/.NET MCP server ready to be deployed as an Azure Function (one of several languages offered). The sample implemented a couple of tools and my goal was to implement an additional tool that would accept an IP address and return the country where that IP address is registered. The IP to country code mapping functionality if available as part of Azure Maps.
I started to hand-implement it, then… I decided to see how far GitHub Copilot Agent mode would get me. I’ve used it many times before and it can be helpful, but this ask was tricky. One challenge being that there was IaC in the mix: Bicep files to support the azd up deployment, AVM modules, and many code files implementing the feature set. And MCP is still new. And the MCP support within Azure Functions was newer still.
Give GitHub Copilot Agent a Goal
The first step was to give the GitHub Copilot Agent a goal that matches my needs. In my case, I gave Agent mode this prompt:
The .NET project implements a couple of Model Context Protocol (MCP) tools – a couple for snippets and one that says hello. Add a new MCP tool that accepts an IPv4 IP address and returns the country where that IP address is registered. For example, passing in 8.8.8.8, which is Google’s well-known DNS server address, would return “us” because it is based in the USA. To look up the country of registration, use the Azure Maps API.
And here’s what happened – as told through some screenshots from what scrolled by in the Agent chat pane – in a sequence that took around 12 minutes:
I can see some coding progress along the way:
A couple of times the Agent paused to see if I wanted to continue:
It noticed an error and didn’t stop – it just got busy overcoming it:
It routinely asked for permissions before certain actions:
Again, error identification – then overcoming errors, sometimes by getting more up-to-date information:
Second check to make sure I was comfortable with it continuing – this one around 10 minutes after starting work on the goal:
In total 9 files were changed and 11 edit locations were identified:
Deploy to Azure
Using azd up, get it deployed into Azure.
Add MCP Reference to VS Code
Once up and running, then I installed it in VS Code as a new Tool – first click on the wrench/screwdriver:
Then from the pop-up, scroll the the bottom, then choose + Add More Tools…
Then follow the prompts (and see also instructions in the GitHub repo):
Exercise in VS Code
Now that you’ve added the MCP server (running from an Azure Function) into the MCP host (which is VS Code), you can invoke the MCT tool that accepts an IP and returns a country code:
domain-availability-checker% dig A en.kremlin.ru +short
95.173.136.70
95.173.136.72
95.173.136.71
domain-availability-checker%Using the first of the three returned IP addresses, I ask within the Agent chat area “where is 95.173.136.70 located?” – assuming that the LLM used by the chat parser will recognize the IP address – and the need for a location – and figure out the right MCT tool to invoke:
I give it one-time permission and it does its thing:
Victory!
Check Code Changes into GitHub
Of course, using GitHub Copilot to generate a commit message:
Done!
Connect with Bill and Boston Azure AI
