The Worst Coder in the World goes agentic: building a leaderboard cracking AI

The Worst Coder in the World goes agentic: building a leaderboard cracking AI - Stack Overflow

Stack Overflow Business Stack Internal: the knowledge intelligence layer that powers enterprise AI.Stack Data Licensing: decades of verified, technical knowledge to boost AI performance and trust.Stack Ads: engage developers where it matters — in their daily workflow.I’ll just come out and say it: I’m not very tech savvy—obviously, since I’m writing a piece where I proudly call myself the Worst Coder in the World. But I like to think my technical deficiencies have a bit of a charm to them, almost like I’m a tech ingénue—unsophisticated but genuine and curious. As my editor, Ryan Donovan, put it, “You’re not afraid to ask the simple questions.”I take this to be Ryan’s very nice way of saying I’m not afraid to ask the dumb questions. And he’s right—I’m not afraid to ask dumb questions. Things like:What does node.js do?What’s the difference between frontend and backend development?How do I upload this file to GitHub?Wait, can you check that I actually uploaded it to GitHub or did I do it wrong?How do I share my GitHub with you so you can check if I did it wrong?Luckily, AI has gotten really good at answering these kinds of questions, so curious minds like mine are efficiently and quickly satiated. And really, the barrier to entry for all things tech-related, not just knowledge, has been lowered significantly by AI. Anyone can vibe-code a working app in just a few hours—even people with zero coding experience, like me.For tech ingénues like myself, it opens up a world of possibilities that were once completely out of reach. The world is my software oyster, so to speak. However, I’m not actually great at coming up with ideas for useful things (e.g. my priorly vibe-coded poop app), and I’m a bit of an AI snob, besides. My hesitancy to use AI tools has probably cost me some Hours of Productivity, but really, I’m just trying to keep my brain from turning to mush—well, mushier than it already is.Still, I reckon there are plenty of things AI could help me with, including helping me create bespoke AIs that are fashioned to my specific needs. Because I’m not a great generator of useful ideas, and because my lack of technical experience means my imagination for software is quite limited, I actually asked Gemini to come up with ideas for what I could build.Unfortunately, I wouldn’t call Gemini a great generator of useful ideas, either, based on the nonsensical concepts it created for me. I had to actively fight off the vindication I felt about my AI hesitancy.So back to the drawing board. I pondered and ruminated on the things a custom AI agent could help me with. What were things I had always wanted to do that I was either too busy, too inexperienced, too insecure, or too lazy to do on my own?And then it hit me: the Stack Internal Leaderboard.Okay, context. If you don’t know, Stack Internal is our company’s enterprise knowledge product. It has all the knowledge sharing you know and love from Stack Overflow with the added bonus of not leaking trade secrets to the world. Companies use Stack Internal to store and validate their organization’s information—everything from their PTO policy to their coding guidelines. Teams can document and share their important context with other departments, and people can ask questions or find answers about the happenings at their org. Then, people can accept answers, upvote Q&A pairs, and comment on posts, allowing the best, most important stuff to bubble to the top. We call that human-validation, baby.Now that agents are a thing, all that knowledge sitting in a company’s Stack Internal has a new use case. Companies can connect their Stack Internal instance—the one bursting at the seams with human-validated context—to their agents via the Stack Internal MCP server.I promise that wasn’t just a commercial break for Stack Internal. We use Stack Internal company-wide at Stack Overflow, and the people who gain the most reputation points every week get a shoutout in the weekly email from our CEO, Prashanth Chandrasekar. We call this The Leaderboard. I first noticed The Leaderboard rankings when covering for a good Stacker pal of mine on vacation, who tasked me with pulling the weekly Leaderboard stats. The first time, I only recognized a couple of the names placed in this coveted weekly rank, all of them people I had worked with directly. But over time, I started recognizing more and more names. The more I saw those repeating names, the more I got curious about them. I’d look them up on Slack or click on their Stack Internal profiles, reading the questions and answers that had elevated them to their elite seats on The Leaderboard.I began to wonder—was this the equivalent of being Stacker famous?In my year here, I had never posted a single question or answer on our Stack Internal instance. I had only been updating existing Q&As with up-to-date information. What a waste of a whole 365 days! I had given away my hard-earned reputation points to posts written by my predecessors, blocking out my own star by laboring away in the name of others. But no longer. I was going to crack The Leaderboard and get into Prashanth’s weekly email. My name would be seen and remembered by my fellow Stackers. If I created an AI agent that connected to our Stack Internal MCP server, my spot on the weekly CEO email—and the well-deserved Stacker fame that would come with it—was just a few careful prompts away. But first, I needed to know…What the heck is an MCP server?Although I could have easily found this answer online, I figured it would be a waste of my talent for fearlessly dumb questions. For this one, I took to pestering my pal Ben Marconi, Stack’s Director of Ecosystem Strategy, for answers. I went into our call with a clear end goal—learn as much as possible about MCP so I could create the perfect Leaderboard cracking agent.I had a lot of questions for Ben. I mean, I really didn’t know much about the tech going into my call with him. I’ll share my full interview with him later on this blog (so keep your eyes peeled for that), but in the meantime, allow me the pleasure of summarizing my learnings for you. To start, I clumsily asked Ben, “Uhhhh, what exactly is an MCP server?” Ben, gracious and patient as ever, explained the following to me:MCP—which stands for Model Context Protocol—is an AI standard that allows LLMs to securely connect to outside data sources. He told me to think of it as a “standardized bridge that connects new cutting-edge AI functionality to all the other stuff—all the other tools that exist in the software world.” We do this in the name of efficiency and usability. In order to make these AI agents as useful as possible, they need access to outside sources of information and may even need the permissions to act on our behalf in those places.In the past, when people wanted LLMs to access external information or perform tasks for them, they would have to create custom connectors for their AIs. They’d do this by building a code-based access point, attached to an external company’s API. “You can think of [APIs] as the window between the restaurant and the kitchen,” Ben said. “You can pass information through the window in the form of code, and then that information can be structured. You can repeat this over and over and over again in the two systems you have configured to talk to each other.”I asked Ben why we didn’t just keep making our own connectors instead of using MCP. Why not build a bunch of windows in the restaurant so all the information could be passed through? Well, that’s because you’d have to build a bunch of windows into your restaurant…by hand. Ben explained that these API connectors needed custom code to be compatible with each tool’s API. Building them has been historically time consuming and extremely complicated. “Let's say we’ve got software Product A, Product B, and Product C,” Ben said, “These products are from different companies and they all have their own APIs, which allow a person to interact with that system's data through a programming language. The problem is each of those three APIs are probably configured to work a little bit differently.”“And let's say we want to connect them all to a third tool that sits up above them [like an agent]. I want to plumb all of them into one location. Historically, the challenge is it can take a lot of custom configuration to understand Product A's API, Product B's API, and Product C’s API. So when you're building these large interconnected systems, it starts to get pretty difficult and complex. You have to understand how each of these programming interfaces work and how to write the code to structure those communications back and forth.”I was picking up what Ben was putting down. It all did seem pretty complicated, especially when you consider that hundreds and maybe even thousands of custom connectors would need to be built, just so our agents can access the same suite of tools we use on a daily basis.So if custom plumbing doesn’t work, why does MCP? As Ben described it to me, MCP sits a layer above existing APIs, standardizing the external data being fed to it. This standardized data is organized so AI agents can automatically understand it, allowing for significantly faster connection to outside tools and data. Basically, instead of creating a custom connector for each API that needs translating, you could plug all the APIs into one universal translator—an MCP server. With much less effort than before, you can hook your LLM up to whatever external data source you want, giving it access to the context it needs to actually be useful to you (id est the context part of Model Context Protocol).Ben emphasized that MCP is a decently new standard, having only been created by Anthropic a few years ago. Still, he called it a “relatively elegant solution” and one that people are rightfully excited about. “We need a more efficient means of supplying context to the agent layer,” Ben said. “One that could potentially allow agents to share informa