The MCP-First Revolution: Why Your Next Application Should Start With a Model Context Protocol Server
By Ravi Kiran Vemula, VP of Engineering @ CAW | MCP Ecosystem Builder | AI Agent Architect
In the rapidly evolving software development landscape, a new paradigm is quietly taking hold: MCP-first development. This approach flips traditional development upside down by prioritizing the creation of a Model Context Protocol (MCP) server before designing any user interface. This shift transforms how developers build applications and enables unprecedented AI integration capabilities.
As someone who’s been deeply involved in building the MCP ecosystem — including leading the initial implementation of FireCrawl MCP Server with over 1.5k stars and contributing to LangChain’s MCP adapters — I want to share why this approach matters and how it’s already changing the development of tools across industries, from game engines to digital audio workstations and design software.
What Is Model Context Protocol (MCP)?
Before diving into why MCP-first development matters, let’s clarify what MCP is.
The Model Context Protocol is an open-source framework introduced by Anthropic in late 2024 that standardizes how AI models connect with external tools and data sources. It creates a structured communication channel between AI agents (like Claude) and software applications, allowing the AI to:
- Discover what capabilities an application has
- Interact with the application’s features through natural language
- Retrieve information from the application when needed
- Execute commands within the application based on user requests
In essence, MCP bridges natural language and application functionality, enabling AI models to become powerful interface layers for complex software.
The Traditional Development Approach vs. MCP-First
Traditionally, software development follows a familiar pattern:
- Define core functionality
- Design a user interface
- Build both simultaneously
- Release, iterate, and expand
This approach makes sense when humans are the only expected users of your application. However, in today’s AI-integrated world, it can create unnecessary limitations.
The MCP-first approach rethinks this process:
- Define core functionality
- Build an MCP server that exposes this functionality
- Test with AI models as users
- Build traditional UI interfaces later (if needed)
The key difference is that your application is AI-compatible from day one, and its core functionality exists independently of any specific interface paradigm.
Why Build an MCP Server Before Your UI?
1. Focus on Core Functionality
When you build an MCP server first, you’re forced to think deeply about your application’s essential capabilities separate from how they’ll be presented to users. This clean separation of concerns often results in more modular, maintainable code.
“Building the MCP server for Blender forced us to carefully consider which operations were truly essential,” explains Siddharth Ahuja, creator of Blender MCP. “This clarified our thinking and made the core functionality more robust.”
2. Built-In AI Integration
The most obvious benefit of an MCP-first approach is immediate AI compatibility. Your application becomes instantly accessible to AI-powered tools like Claude Desktop, Cursor, Windsurf, or Cline.
For example, the Figma MCP server created by Greg Lipstein allows AI coding tools to directly access Figma design data, enabling them to generate accurate code from designs without screenshots or manual recreation.
3. Interface Flexibility
When your application’s functionality is exposed through an MCP server, you gain extraordinary flexibility in how users interact with it:
- Traditional GUI for direct manipulation
- CLI for power users and automation
- AI interfaces for natural language interaction
- API access for third-party integration
- All without duplicating core business logic
This means you can adapt to new interface paradigms as they emerge without rewriting your entire application.
4. Accelerated Development Cycles
Testing functionality becomes dramatically simpler when you can interact with it through natural language. Need to check if a specific feature works? Simply ask an AI to use it, rather than clicking through multiple UI screens or writing test scripts.
Miguel Tomas, creator of the Unity MCP server, highlights this benefit: “With MCP, we can test complex game development workflows by simply asking Claude to execute them. This has reduced our testing time significantly.”
5. Enhanced Collaboration
MCP enables new forms of collaboration between humans and AI. Designers can work with AI to rapidly iterate on designs, developers can ask AI to implement specific features, and content creators can use AI assistance to enhance their workflows.
MCP Success Stories in the Wild
Several pioneering projects have already implemented MCP servers for popular applications, with impressive results. Many of these have become cornerstone projects in the MCP ecosystem that I’ve been privileged to contribute to:
Blender MCP
Blender MCP (7.6K+ GitHub stars) connects Blender to Claude AI, enabling:
- Natural language 3D modelling and scene creation
- Two-way communication for real-time feedback
- Access to the full power of Blender through conversation
“I was able to create a complex low-poly scene with proper materials just by describing it to Claude,” reports one user. “What would have taken me hours took minutes.”
Figma Context MCP
Figma Context MCP (2.6K+ GitHub stars) gives AI-powered coding tools access to Figma design files:
- Accurate code generation directly from Figma designs
- Structured access to layout and style information
- Reduced manual implementation time
When used with Cursor, developers report 2–5x faster UI implementation times compared to traditional coding approaches.
Unity MCP
Unity MCP provides AI models with direct access to the Unity Editor:
- Execute menu items through natural language
- Select and manipulate game objects
- Run tests and receive console logs
- Install packages and manage project assets
Game developers using Unity MCP report being able to automate repetitive tasks and focus more on creative aspects of their work.
Ableton MCP
Ableton MCP connects Ableton Live with Claude AI:
- Create and manipulate MIDI and audio tracks through conversation
- Select instruments and effects via natural language
- Control playback and editing
- Generate musical ideas based on descriptions
Musicians can now say “Create an 80s synth-wave track” and watch as Claude sets up appropriate instruments, MIDI patterns, and effects.
Unreal MCP
Unreal MCP brings similar capabilities to Unreal Engine:
- TCP server implementation for remote control of Unreal
- JSON-based command protocol
- Comprehensive scene manipulation
- Python companion scripts for client-side interaction
How MCP Servers Work: A Technical Overview
At a technical level, MCP servers typically consist of several key components:
1. The Application Bridge
This component interfaces directly with the target application, often using the application’s native API or plugin system. For example:
- Blender MCP uses Blender’s Python API
- Unity MCP uses Unity’s C# scripting capabilities
- Ableton MCP integrates via Ableton’s MIDI Remote Script system
2. The MCP Protocol Implementation
This standardized layer handles communication between AI models and the application bridge. It implements:
- Tool discovery: Allowing AI models to learn what capabilities are available
- Command parsing: Interpreting AI requests into application commands
- Response formatting: Packaging application data for AI consumption
- Error handling: Gracefully managing failures and providing feedback
3. Communication Channels
Most MCP servers use one of two communication methods:
- HTTP/WebSockets: For standalone applications
- Standard input/output (stdio): For direct integration with AI tools like Claude Desktop
Implementing Your Own MCP Server: Best Practices
If you’re considering an MCP-first approach for your next application, here are some best practices to consider based on my experience building and contributing to several MCP implementations:
1. Start With Core Functionality
Identify the essential operations your application needs to perform, independent of how users might access them. These become your MCP “tools.” When we built FireCrawl MCP, we began with the most fundamental web scraping operations before expanding to more complex capabilities like deep research.
2. Design for Discoverability
AI models need to understand what your application can do. Provide clear tool descriptions, parameter documentation, and examples. This is crucial for AI integration success — models like Claude or GPT need to easily grasp when and how to use your tools.
3. Implement Robust Error Handling
AI models will test the boundaries of your application’s capabilities. Ensure your MCP server gracefully handles invalid requests and provides helpful error messages. For FireCrawl, we implemented automatic retries with exponential backoff for transient errors, which significantly improved reliability.
4. Consider Security from Day One
Since MCP servers expose application functionality to external agents, implement appropriate authentication, authorization, and input validation. For enterprise applications, consider how to maintain data privacy while still allowing AI access to core functionality.
5. Build Incrementally
Start with a minimal set of well-implemented tools, then expand as needed. This allows you to validate the approach before committing extensive resources. In my experience leading MCP implementations, the most successful projects begin with a focused scope and grow organically based on actual usage patterns.
Challenges and Limitations
While MCP-first development offers significant advantages, it’s not without challenges:
Learning Curve
The MCP specification is relatively new, and there’s a learning curve associated with implementing it correctly.
Limited Tooling
As an emerging standard, MCP doesn’t yet have the same level of development tools and utilities as more established protocols.
AI Model Limitations
Current AI models sometimes struggle with complex APIs or special-case handling, requiring careful tool design to ensure reliability.
Security Considerations
Exposing application functionality to AI models requires thoughtful security design to prevent unintended consequences or misuse.
The Future of MCP-First Development
As AI continues to evolve as an interface paradigm, MCP-first development is likely to become increasingly mainstream. We can expect:
- More applications shipping with built-in MCP servers
- Development tools specifically designed for MCP server creation
- Standards for MCP security and authentication
- AI models optimized for MCP interaction
The most exciting prospect is how this approach democratizes software development: when AI can directly interact with applications, users can accomplish complex tasks through natural language without needing to learn specialized interfaces.
Getting Started with MCP
If you’re interested in exploring MCP-first development, here are some resources to get started:
- Visit modelcontextprotocol.io for official documentation
- Explore the GitHub repositories mentioned in this article
- Join the communities forming around specific MCP implementations
- Experiment with Claude Desktop connected to existing MCP servers
Conclusion: The Strategic Advantage of MCP-First
In a world where AI is increasingly becoming the primary interface between humans and software, building an MCP server first is more than just a technical choice — it’s a strategic advantage.
By making your application’s functionality available through the Model Context Protocol, you’re future-proofing your software, enabling new forms of human-AI collaboration, and potentially reaching users who would be intimidated by traditional interfaces.
The question for developers today isn’t whether to implement MCP, but when and how to do so most effectively. As we’ve seen from the success of projects like Blender MCP, Figma Context MCP, and others, the benefits can be transformative.
Is your next project going to be MCP-first? The growing evidence suggests it probably should be.
About the author: Ravi Kiran Vemula is VP of Engineering at CAW Studios, where he leads AI innovation focused on code review and test generation. He’s a significant contributor to the MCP ecosystem and has led the initial implementation of FireCrawl MCP Server along with contributing to LangChain’s MCP adapters. His work focuses on creating self-hosted agentic development solutions and building bridges between AI assistants and real-world capabilities. Connect with him on LinkedIn or read more of his insights on Medium.
