4-4 How to Boost Development Productivity with MCP?
May 12, 2025
This article is part of our Cursor workflow series for engineers.
You know that feeling when you're deep in code, and suddenly you need to switch to your browser to check a ticket, then to another tab to run tests, then back to GitLab to create a merge request? Each context switch breaks your flow and eats away at your productivity.
I used to spend chunks of my day jumping between tools. Write code in my editor, copy requirement details from Linear, manually test features in the browser, write commit messages, and navigate to GitLab to create pull requests. It felt like being a human API orchestrator instead of actually building things.
This is exactly what MCP solves. Instead of you adapting to your tools, your tools adapt to your workflow.
Why MCP Changes Everything
Without MCP, Cursor's AI agent can already help with many tasks:
- Writing design documents
- Modifying existing documentation
- Writing tests
- Writing code
But these capabilities stay locked inside the editor. Even with AI, you still need to manually bridge the gap between your code and the external tools that drive your actual work.
MCP connects these dots. When a new requirement comes in, you can pull ticket information directly through Linear's MCP. After Cursor helps implement the feature, you can validate it through Playwright MCP. Once everything looks good, you can create a pull request through GitHub or GitLab MCP—all without leaving your editor.
Real-World Example: MCP-Integrated Development Flow
Let me show you how this works with a concrete example. I'll walk through building a simple todo list feature using three different MCP integrations.
Step 1: Fetching Requirements with Linear MCP
When the product team creates a ticket, instead of manually copying and pasting requirement details, you can let Cursor pull this information directly from Linear.
Cursor calls the Linear MCP tool directly. It finds the ticket, reads the requirements, and starts planning the implementation based on the existing codebase.
Step 2: Automated Validation with Playwright MCP
Manual testing works, but automation is better. This is where Playwright MCP comes in.
You can ask Cursor: "Use Linear MCP to find this requirement, then use Playwright MCP to validate the implementation."
Cursor pulls the ticket details from Linear, then uses Playwright MCP to interact with the development environment. It clicks through the app, tests the delete functionality, and confirms everything works as expected. Based on these results, it writes an end-to-end test.
Step 3: Submitting Code with GitLab MCP
With the feature implemented and tested, it's time to commit and create a pull request. GitLab MCP handles this final step.
First, stage the changes and ask Cursor to write a commit message based on the new functionality. After committing, push the code to a remote branch.
Then use this prompt: "Based on the differences between the current branch and the main branch, use GitLab MCP to create a merge request."
Cursor calls the create_merge_request tool and generates a merge request on GitLab using the commit history.
Compare this to the manual process from Chapter 3, where we had to copy descriptions to GitLab manually, now everything happens from within Cursor.
The Bigger Picture
What you just saw isn't about any single tool being particularly impressive. It's about eliminating the friction between different mundane tasks. When your AI agent can seamlessly move between requirement gathering, implementation, testing, and deployment, you spend more time solving actual problems and less time managing tools.
The workflow becomes: think about what you want to build, communicate it to Cursor, and let the MCP integrations handle the orchestration. Your editor becomes a control center for your entire development process.
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