AI

Implementing Agent Skills and MCP servers as part of my development workflow has been a huge boost to my productivity. Long gone are the days where I’d actually have to leave my IDE to raise a pull request in GitHub, and then manually add that PR to a Jira ticket that’s still impossible to find in 2026. Now it’s just a matter of me asking GitHub Copilot to create the PR using my create-pr Agent Skill, then use the Atlassian MCP Server to link the PR to the appropriate Jira ticket. All of which I can do without ever having to leave my IDE. ...

The World Cup is over, and despite England losing in the semi-finals, there were plenty of successful hatewatches to be enjoyed (Thank you Belgium and Spain for your service). If you’re sad that it’s all over, the Premier League is just over 1 month away. Last season, my team Arsenal finally won the title after a 22-year drought. Arguably, we should have won the EFL Cup and the Champions League. But as any Arsenal fan will tell you, we do have a tenancy to lead by a goal, and just kill the game until the final whistle. Our defence is probably the best in the world (Our biggest defeat last season was a 2-0 loss to Man City), but our attacking could, and should be much better. ...

My side project (Biotrackr) started as a Fitbit data tracker and has gradually evolved into a full multi-agent system. Currently, it tracks my health data from Fitbit including sleep patterns, activity levels, food intake, and weight. I’ve recently built a chat agent that allows me to ask questions about my health data through the UI. However, I wanted to take this a step further and use another agent to build charts and graphs to visualize my data. ...

When building an MCP server for my side project (Biotrackr), one of the first things I had to think about was how to manage secrets. The MCP server needs an APIM subscription key to call downstream health data APIs, and it also exposes an API key for clients connecting to it. That’s two secrets that need to be stored, distributed, and protected and if either one leaks, the blast radius could extend across the entire platform. ...

When I first built an AI Agent for my personal health platform Biotrackr, I used the Microsoft Agent Framework’s function tool pattern with 12 tools decorated with [Description] attributes, registered via AIFunctionFactory.Create(). This wasn’t ideal as I already had an MCP Server exposing the same 12 tools to VS Code and other MCP clients. The natural approach would have been to make the Chat API another MCP client. But there was a blocker. The Anthropic provider in Microsoft Agent Framework (Microsoft.Agents.AI.Anthropic) didn’t support Local MCP Tools. If I wanted to use Claude as the LLM backend (which I did), function tools were the only option. So I duplicated all 12 tool implementations in the Chat API, complete with their own models, validation logic, and API call code. ...