Practical MCP Use: Real-World Workflows with Model Context Protocol
In the ever-evolving landscape of developer productivity, finding tools that genuinely streamline your workflow is a constant challenge. Over the past few months, I’ve been experimenting with the Model Context Protocol (MCP) in Cursor, and I’ve published four open-source MCP servers that have become essential to my daily routine. In this post, I’ll walk through what MCP is, introduce each server, and share some honest thoughts on their practical value at this point in time.
What is MCP (Model Context Protocol)?
MCP stands for Model Context Protocol: an open standard that acts like a USB-C port for AI applications. Just as USB-C lets you plug in all sorts of gadgets without a drawer full of adapters, MCP provides a universal way to connect large language models (LLMs) to your data, tools, and workflows, whether they’re on your laptop or floating somewhere in the cloud.
At its core, MCP is a client-server protocol. You run lightweight MCP servers that expose specific capabilities (like reading files, querying databases, or, say, wrangling your Hugo frontmatter), and your AI tools (the clients) connect to these servers to get the context they need. This means you can swap out LLMs, tools, or data sources without rewriting everything from scratch. It’s open source, cross-platform, and has SDKs for Python, TypeScript, Java, C#, and more. Read more about MCP on the official site.
Why does this matter? Because LLMs are only as smart as the context you give them. MCP makes it easy to build agents and workflows that can actually do useful things with your data, without a mess of custom integrations. (Anthropic announcement)
There are also just many tasks that are pretty easy in something like python but very hard for models at this point (for example: counting characters in a phrase). Why do things the hard way?
Four Practical MCP Servers in Action
Here are the four MCP servers I’ve published and actively use:
1. hugo-frontmatter-mcp
What it does:
This server makes it trivial to read, update, and validate frontmatter in Hugo markdown files. It’s perfect for content creators and static site maintainers who want to automate metadata management: think batch updating tags, fixing dates, or ensuring consistency across posts.
I found that models on their own struggled with writing malformed YAML, making inconsistent format updates, and other minor annoyances. This has completely eliminated that.
How I use it:
I use this MCP server to quickly update post metadata without leaving Cursor. It’s especially handy for bulk operations, like renaming tags or validating date formats before publishing.
2. mutmut-mcp
What it does:
A wrapper for the mutmut mutation testing tool, this MCP server lets you run mutation tests and view results directly in your editor. It’s a great way to ensure your Python tests are robust and actually catching bugs.
How I use it:
Whenever I’m working on high-reliability Python code, I’ll run mutmut-mcp to check my test coverage. The feedback loop is fast, and I can see exactly which lines need better tests. With the MCP an agent can run mutmut, prioritize “survivors”, write new tests that cover those cases, run the tests to validate they pass, then re-run mutmut to validate that they covered the identified cases. It’s pretty hands off and works well.
3. todolist-mcp
What it does:
A simple, local-first todo list manager that lives in your editor. It’s designed for developers who want a lightweight way to track tasks without relying on external apps or cloud services.
How I use it:
I keep a running list of project-specific todos right in my workspace. It’s frictionless, and because it’s local, I don’t have to worry about syncing or privacy. This is mostly something I use to keep track of the many things that come up in a coding session but aren’t urgent. For little personal projects I’m not using something like Jira, this isn’t work.
4. writing-tools-mcp
What it does:
A set of writing and editing utilities (think readability analysis, keyword density, and spellchecking) tailored for markdown and technical writing. It’s like having a mini writing assistant built into your editor.
How I use it:
I use this MCP server to polish blog posts and documentation. The instant feedback on readability and keyword usage helps me write clearer, more effective content. It’s incredibly useful for things like making sure titles are an appropriate length: models struggle with this to be point they basically can’t do it. This of course makes sense when you think about how they’re tokenizing, but it feel silly as a user.
Honest Assessment: Are MCP Servers Worth It?
The Good:
- Seamless Workflow: The biggest win is staying in Cursor. No more jumping between apps or terminals.
- Task Automation: Repetitive tasks (like updating frontmatter or running tests) become totally easy with the agent.
- YOLO-mode You can set up cursor to do these tool calls without approvals, which means more cycles the agent can do without your involvement.
- Customizability: Each MCP server is focused and hackable. If I need a tweak, it’s just Python.
The Tradeoffs:
- Learning Curve: Setting up MCP servers takes a bit of initial effort, especially these that are locally hosted.
- Niche Use Cases: Some servers (like mutmut-mcp) are only useful if you’re already using the underlying tool.
- Maintenance: As with any open-source tool, keeping MCP servers up-to-date with editor changes or new workflows is an ongoing task.
Bottom Line:
If you spend a lot of time in your editor and value automation, MCP is a game-changer. It’s not for everyone yet, but for power users and tinkerers, it offers a level of control and efficiency today that’s hard to match. I think as the ecosystem develops these are going to become much more ubiquitous and powerful, but it’s early.
Links to the MCP Servers:
Further Reading:
Subscribe to the Newsletter
Get the latest posts and insights delivered straight to your inbox.