VS Code and Cursor Now Support MCP: Opening AI Access to Custom Data and Actions

Big news for developers building AI-enhanced workflows: Visual Studio Code and Cursor now support Model Context Protocol (MCP) configurations. This means both environments can now connect to custom external data sources or action endpoints—bringing personalized, context-rich AI assistance right into your development workflow.

Let’s unpack what that means, why it matters, and how you can take advantage of it.

What Is MCP?

Model Context Protocol (MCP) is a specification proposed by Anthropic to create a standardized way for AI models (like Claude, GPT-4, etc.) to retrieve external context or trigger external actions.

In simple terms, MCP acts like a universal adapter for AI systems to interact with various data sources or tools. Whether it’s a proprietary database, an internal API, a cloud function, or even an entire ERP system—if you can expose it through MCP, your AI can use it.

Here’s a typical use case:

Plain English interface on a database
A non-technical user wants to query a company’s internal sales database. Instead of learning SQL, they just type a question in plain English. An AI assistant connected via MCP interprets the query, fetches the right data, and returns a clean answer.

The beauty of MCP is that this kind of setup can be reused across different models and environments—no need to rewrite adapters for each one.

How MCP Works (High-Level)

MCP introduces a formal way to define:

• Datasets – structured or unstructured information your AI can read from.
  
  
• Tools – actions the AI can trigger (e.g., "send an email", "create a Jira ticket").
  
  
• Interfaces – schemas that describe how to call into these sources or tools.
  
  
• Servers – endpoints where these definitions live and respond to requests.
  
  

When a model like Claude or GPT-4 is given access to an MCP server, it can dynamically discover what tools or data are available and query or use them in a standardized way.

Think of it like plugging your AI into an API catalog with rich metadata and semantics.

Why VS Code and Cursor Integration Matters

Until now, MCP was mostly theoretical or limited to controlled environments. The recent updates to VS Code and Cursor (a fast-growing AI-native code editor) mean developers can now:

• Configure local or remote MCP servers
  
  
• Connect models to their own datasets or internal APIs
  
  
• Use these sources in real-time AI assistance directly within the editor
  
  

For example, you could:

• Attach a bug tracking system as a tool via MCP and ask your AI assistant to find the top open issues.
  
  
• Connect a documentation repo and ask, “How does our internal auth library work?”
  
  
• Plug in a financial dataset and use the AI to generate a revenue report without ever leaving your IDE.
  
  

This turns VS Code and Cursor into fully extensible AI workstations, not just code editors.

Getting Started

To use MCP in VS Code or Cursor:

1. Set up an MCP server – You can build one in any language; it's just a well-defined interface.
   
   
2. Define your datasets or tools – Format them according to MCP specs.
   
   
3. Point your editor to the MCP config – Both VS Code and Cursor now allow this via settings or config files.
   
   
4. Start using AI with live context – The model will now see and interact with the external sources you defined.
   
   

The integration of MCP into developer tools like VS Code and Cursor is a big step forward for practical, extensible AI. It means more control over what your AI knows and does, tailored to your data, your workflow, and your environment.

As MCP adoption grows, expect to see more shared MCP endpoints, open-source tools, and collaborative datasets. But for now, if you're building custom AI experiences or just want smarter in-editor assistance, hooking up your own MCP server might be the best way to level up.

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