I Tried Building My Own MCP Tool From Scratch
In the fast-paced world of tech engineering, staying current with the latest advancements is crucial. In this blog, I share my experience building my own MCP (Managed Control Plane) tool from scratch, despite not being a seasoned programmer. Using the Kubernetes-native KMCP solution, I managed to get a working tool that scrapes YouTube videos and generates blog posts automatically.
Choosing KMCP and Getting Started
I chose K agents’ KMCP tool, a Kubernetes-native agentic solution that standardizes MCP development, making it easier to create and manage tools without worrying about hitting standards manually. After downloading the tool and running the CLI inspector, I was surprised at how straightforward and quick the setup was. The tool provides an interactive inspector to test MCP tools locally before deploying.
Building My YouTube Scraper MCP Tool
To solve my problem of having more videos but less time to blog, I built a tool that scrapes my YouTube videos and creates clean, proofed blogs from them. Since my videos include many filler words and conversational points, I wanted the tool to not only scrape transcripts but also polish them for readability. I didn’t write most of the Python code myself; instead, I used ChatGPT to help generate it.
The MCP tool fetches video information and transcripts using the YouTube API, which required setting up a Google Cloud project and getting an API key. The tool runs locally without Kubernetes, demonstrating flexibility and ease of debugging.
Exploring the MCP Tool in Action
Running the KMCP inspector alongside my tool showed that it successfully discovered and loaded my custom YouTube tool along with the built-in echo tool. I tested it by pasting a video ID from my YouTube history, retrieving the video title, description, and a cleaned transcript. Though not perfect yet, the setup proved effective for my use case.
Future Plans and Reflections
Next, I plan to enhance the tool with authentication and integrate it with agent-to-agent communications for more complex workflows. Ultimately, the goal is for the tool to automatically generate blog pull requests on GitHub Pages, alerting me when a blog post is ready for review.
Reflecting on why use MCP tools versus standalone scripts, I realized MCP tools provide a standardized, reusable framework that anyone can run with minimal configuration. This approach opens doors for more people to build powerful tools and promotes adoption in the Kubernetes ecosystem.
Takeaways
- KMCP offers an approachable way to build MCP tools quickly with minimal setup.
- Leveraging AI assistance like ChatGPT can accelerate development, especially if you’re not a seasoned coder.
- MCP tools standardize and simplify integration with Kubernetes-native workflows and tools.
- Running MCP tools locally before deploying helps with debugging and iteration.
- This approach is great for automating repetitive tasks and can be extended for more complex workflows with authentication and agent communication.
