8/11/2025

Automate Your Stand-up: Building an MCP Server for AI-Assisted Daily Check-ins

Hey there. Let's talk about the daily stand-up. That 15-minute meeting that's supposed to be a quick, agile sync-up but often turns into a rambling, off-topic status report that leaves everyone feeling a little bit drained. We've all been there. You're trying to remember what you did yesterday, what you're doing today, & if you have any blockers, all while half-listening to everyone else's update. It's a necessary ritual, but honestly, it can be a drag.

But what if it didn't have to be? What if you could get all the benefits of a daily check-in—alignment, accountability, & unblocking issues—without the meeting fatigue? This is where AI comes in, & not just in the way you might think. We're not talking about a simple reminder bot. We're talking about building a smart, automated system that understands your projects, communicates with your team, & makes the whole process seamless.

And the secret sauce to building something this cool? It's a concept called an MCP server. I know, it sounds a bit technical, but stick with me. It’s a game-changer for anyone who wants to create custom AI solutions that are deeply integrated with their workflow. We're going to dive into what an MCP server is & how you can build one to completely transform your team's daily check-ins.

So, What's an MCP Server, Really?

Alright, let's demystify this "MCP" thing. MCP stands for Model Context Protocol. It was originally developed by Anthropic, but it’s an open standard, which is pretty cool because it means anyone can use it. Think of it as a universal translator or a standardized bridge between an AI assistant (like Claude or a custom-built one) & all the other tools & data sources you use.

Here's the thing: normally, an AI is stuck in its own little world. It can chat with you, but it can't really do anything. If you want it to check your Jira tickets or read a file from your computer, you'd have to build a complex, one-off integration. MCP changes that. It creates a standard way for AI to securely connect to external things like APIs, databases, or even your local file system.

An MCP setup has a few key parts:

MCP Hosts: These are the applications that want to use the AI, like your desktop AI client or a custom app.
MCP Clients: These are the messengers that connect the host to an MCP server.
MCP Servers: This is the heart of it. A lightweight program that you create to expose specific "capabilities" or "tools" to the AI. For example, you could have a tool to "read a file" or "query a database."

So, when we talk about building an MCP server for daily check-ins, we're talking about creating a custom program that gives our AI the specific superpowers it needs to automate our stand-up. It's like building a custom utility belt for Batman, but for your AI.

Why Bother Automating Your Stand-up with a Custom AI?

You might be thinking, "This sounds complicated. Why not just use an off-the-shelf tool?" & that's a fair question. There are some great tools out there like Standuply & Geekbot that can automate stand-ups through chatbots. They're a fantastic option if you want a quick & easy solution.

But building your own system with an MCP server gives you a level of control & customization that you just can't get with a pre-built tool. Here's why you might want to go the custom route:

Deep Integration: You can connect your stand-up bot directly to your specific project management tools, your internal databases, your documentation—anything with an API. This means your bot can provide truly context-aware updates.
Custom Workflows: You're not limited to the "what did you do yesterday, what are you doing today" format. You can design the exact workflow you want. Maybe you want the bot to automatically pull in Git commit messages or check for overdue tasks. With a custom build, you can do that.
Asynchronous & Efficient: The big win is getting rid of the meeting itself. Team members can provide their updates when it's convenient for them, especially in distributed teams across different time zones. The bot can then compile everything into a neat summary that everyone can read in a fraction of the time a meeting would take.
Better Data & Insights: When your stand-up is automated, you're creating a structured log of what everyone is working on. Over time, you can analyze this data to spot patterns, identify recurring blockers, & get a much clearer picture of your team's velocity.

Building your own solution is definitely more work, but the payoff is a tool that is perfectly tailored to your team's unique needs.

Building Your Stand-up MCP Server: A High-Level Guide

Okay, let's get into the fun part: how would you actually build one of these things? We'll walk through a high-level guide using Python as an example, but the concepts are the same for other languages with MCP SDKs like TypeScript.

The core idea is to create an MCP server that exposes a few key "tools" to an AI assistant. These tools will allow the AI to interact with Jira to get task information & with Slack to post the stand-up summary.

Step 1: The MCP Server Foundation

First, you'd need to set up a basic MCP server. The official MCP SDKs make this surprisingly straightforward. You'd install the necessary library & then write a small script to create the server instance.

Here's a super simplified "hello world" in Python to give you an idea: