Building AI-Ready Applications with MCP-First Development

Generative AI tools have immense potential, but for them to be truly impactful, they need more than just their training data. For AI to deliver meaningful outcomes, it needs to be grounded in real-time context and have the ability to take actions within external applications. This combination of context-awareness and interoperability is what turns AI from a passive assistant into an active collaborator.

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.

Being 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 — we 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 exploring why MCP-first development matters, it’s important to clarify what MCP actually is.

Model Context Protocol (MCP) is an open-source framework introduced by Anthropic in late 2024. It standardizes how AI models connect with external tools and data sources.

MCP creates a structured communication channel between AI agents (such as Claude) and software applications, allowing the AI to:

1. Discover an application’s capabilities
2. Interact with the application’s features through natural language
3. Retrieve information from the application when needed
4. 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, well-established pattern:

1. Define core functionality
2. Design a user interface
3. Build both simultaneously
4. 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 impose unnecessary limitations.

The MCP-first approach rethinks this process:

1. Define core functionality
2. Build an MCP server that exposes this functionality
3. Test with AI models as users
4. 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

Building an MCP server first forces you to think deeply about your application’s essential capabilities, independent of 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 of this is possible without duplicating your 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 unlocks 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 we’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 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 the 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 are typically composed 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 several best practices to guide the process — drawn from hands-on experience building and contributing to multiple MCP implementations:

1. Start with Core Functionality

Begin by identifying the essential operations your application needs to perform, independent of how users might eventually 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 step 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 inevitably test the boundaries of your application’s capabilities. Make sure your MCP server can handle unexpected or invalid requests gracefully — and respond with 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, it is crucial to 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 our experience during MCP implementations, the most successful projects begin with a focused scope and grow organically based on actual usage patterns.

Challenges and Limitations of MCP-First Development

While MCP-first development offers significant advantages, it’s not without a few notable challenges:

1. Learning Curve

As the MCP specification is relatively new, there’s a learning curve associated with implementing it correctly.

2. Limited Tooling

Being an emerging standard, MCP doesn’t yet have the same level of development tools and utilities as more established protocols.

3. AI Model Limitations

Current AI models sometimes struggle with complex APIs or special-case handling, requiring careful tool design to ensure reliability.

4. 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:

1. More applications shipping with built-in MCP servers
2. Development tools specifically designed for MCP server creation
3. Standards for MCP security and authentication
4. AI models optimized for MCP interaction

Perhaps 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

For those looking to explore MCP-first development, here are some useful resources to begin your journey:

1. Visit modelcontextprotocol.io for official documentation
2. Explore the GitHub repositories mentioned throughout this article
3. Join the growing communities forming around specific MCP implementations
4. Experiment with Claude Desktop connected to existing MCP servers

Conclusion: The Strategic Advantage of MCP-First

In a world where AI is rapidly 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 might otherwise be intimidated by traditional interfaces.

As demonstrated by the success of projects like Blender MCP, Figma Context MCP, and others, the benefits can be transformative.

The real question isn’t whether to implement MCP, but when and how to do so most effectively.

At CAW, we can help you do exactly that. If you’re exploring how to make your application AI-compatible from day one, accelerate development cycles, and improve scalability, we’d be happy to assist. Let’s talk about how MCP-first development can work for you.

FAQs

How is MCP-first development different from the traditional development approach?

Unlike traditional development that begins with UI design, MCP-first development starts by exposing core functionality through an MCP server. It is then tested by AI models as users and traditional UI interfaces are built later if required. The MCP-first approach makes applications AI-compatible from the outset and ensures that the core functionality exists independently of any specific interface paradigm.

Why should developers build an MCP server before the UI?

Building the MCP server first forces a clear focus on core functionality and allows AI models to interact with the app early, making the system more flexible, testable, and scalable in the long run.

Is MCP difficult to implement?

There is a learning curve as MCP specification is relatively new, and the tooling is still maturing. However, starting small with a minimal set of well-implemented tools and then expanding as needed will allow you to validate your approach before committing extensive resources. The most successful projects tend to begin with a focused scope and grow organically based on actual usage patterns.

Are there any security concerns associated with MCP-first development?

Yes, MCP servers expose application functionality to external AI agents. Therefore, a robust security design, including appropriate authentication, authorization, and input validation, should be in place from day one to prevent misuse or unintended consequences.