London, 26 May 2025 — If you've been following the AI development space, you've likely heard the term "MCP" or "Model Context Protocol" being discussed with increasing frequency. But what exactly is MCP, and why is it generating so much excitement among developers and businesses alike? Let's demystify this groundbreaking protocol that's transforming how AI assistants interact with the digital world.
The Simple Explanation: MCP as a Universal Adapter
Imagine you're travelling internationally with various electronic devices. Each country has different plug types, and without the right adapters, your devices are useless. You could carry a bag full of different adapters, or you could have one universal adapter that works everywhere.
MCP is that universal adapter for AI.
Before MCP, connecting an AI assistant like Claude to different tools, databases, or APIs required custom code for each integration. If you wanted Claude to access your GitHub repository, read from your database, and interact with your Slack workspace, you'd need three separate, complex integrations. This created what engineers call an "M×N problem" — M applications trying to connect to N different AI models, resulting in M×N different integrations to build and maintain.
MCP solves this by providing a single, standardised protocol. Now, any tool that supports MCP can instantly work with any AI model that supports MCP. It's elegant, efficient, and revolutionary.
What Makes MCP Different?
1. Open Source and Standardised
Anthropic released MCP as an open-source protocol, meaning anyone can implement it, improve it, and build upon it. This openness has led to rapid adoption and a flourishing ecosystem of MCP servers.
2. Secure by Design
MCP includes built-in security features:
- Explicit permissions: AI assistants can only access what you explicitly allow
- Sandboxed execution: Operations are isolated for safety
- Audit trails: Every action can be logged and reviewed
3. Language Agnostic
While many examples use TypeScript or Python, MCP can be implemented in any programming language. This flexibility means developers can use their preferred tools and existing codebases.
4. Stateful Connections
Unlike simple API calls, MCP maintains context throughout a session. This means Claude can perform complex, multi-step operations while maintaining awareness of previous actions.
How MCP Works: The Technical Flow
Let's look at how MCP actually functions:
- Server Implementation: A developer creates an MCP server that exposes specific capabilities (called "tools") and resources.
- Protocol Registration: The server registers its available tools with the MCP protocol, describing what each tool does and what parameters it accepts.
- AI Connection: When Claude (or another MCP-compatible AI) starts a session, it connects to configured MCP servers and discovers available tools.
- Natural Language Interface: Users can now request actions in plain English. For example, "Check my GitHub pull requests" or "Update the customer database with this information."
- Automatic Execution: Claude translates the natural language request into the appropriate MCP tool calls, executes them, and returns the results.
Real-World MCP Applications
Software Development
User: "Create a new feature branch and set up the basic file structure for a user authentication system"
Claude: [Uses Git MCP to create branch, Filesystem MCP to create files, and returns confirmation]Data Analysis
User: "Analyse last month's sales data and create a summary report"
Claude: [Uses PostgreSQL MCP to query data, processes results, generates insights]Content Management
User: "Publish this blog post after checking it for SEO and UK spelling"
Claude: [Uses Content Publisher MCP to process, optimise, and publish the content]DevOps and Monitoring
User: "Check the status of our Kubernetes pods and restart any that are failing"
Claude: [Uses Kubernetes MCP to check status and perform necessary operations]The MCP Ecosystem
The MCP ecosystem consists of several key components:
1. MCP Servers
These are the building blocks — individual servers that provide specific functionality. Examples include:
- Filesystem access
- Database connections
- API integrations
- Browser automation
- Cloud service management
2. MCP Clients
Applications that can connect to MCP servers. Claude Desktop is the most prominent example, but the protocol is open for any AI application to implement.
3. MCP SDKs
Software Development Kits that make it easy to build MCP servers in various programming languages. The official TypeScript and Python SDKs are the most mature.
4. MCP Registry
Community-maintained directories where developers can discover and share MCP servers. These include the official repository, MCP Hub, and various curated lists.
Why MCP Matters for Businesses
Increased Productivity
By allowing AI assistants to directly interact with business tools, MCP eliminates the constant context switching and manual data entry that slows down workflows.
Reduced Development Costs
Instead of building custom integrations for each AI tool, businesses can leverage the growing library of MCP servers or build one integration that works with all MCP-compatible AI models.
Enhanced Security
MCP's permission model ensures that AI assistants only access what they're explicitly allowed to, providing better control than traditional API key-based systems.
Future-Proofing
As more AI models adopt MCP, investments in MCP infrastructure become increasingly valuable. It's becoming the USB standard of AI integrations.
Getting Started with MCP
For developers interested in exploring MCP:
- Install Claude Desktop: The easiest way to start using MCP is through Claude Desktop, which has built-in MCP support.
- Choose Your First Server: Start with a simple MCP server like the filesystem server to understand the basics.
- Configure and Connect: Add the server to your Claude Desktop configuration and start interacting with your files through natural language.
- Build Your Own: Once comfortable, create your own MCP server for your specific needs using the official SDKs.
The Future of MCP
As MCP adoption grows, we're seeing:
- Enterprise Integration: Major companies are building MCP servers for their internal tools
- Specialised Servers: Industry-specific MCP servers for healthcare, finance, and other sectors
- Enhanced Capabilities: New protocol features for streaming data, real-time collaboration, and more
- Cross-Platform Support: MCP moving beyond desktop to mobile and embedded devices
Common Misconceptions
"MCP is only for Claude"
While Anthropic created MCP and Claude was the first to implement it, the protocol is open for any AI model or application to use.
"MCP is complicated to implement"
Basic MCP servers can be created in under 100 lines of code. The complexity scales with your needs, but starting simple is encouraged.
"MCP replaces APIs"
MCP complements existing APIs by providing a standardised way for AI to interact with them. Your APIs remain unchanged; MCP just makes them AI-accessible.
Conclusion
The Model Context Protocol represents a fundamental shift in how we think about AI integration. By providing a standardised, secure, and efficient way for AI assistants to interact with external tools and data sources, MCP is breaking down the barriers that have limited AI's practical applications.
Whether you're a developer looking to enhance your tools with AI capabilities, or a business leader exploring how AI can transform your operations, understanding MCP is crucial. It's not just another technical protocol — it's the foundation for a new era of AI-augmented productivity.
As we continue to see new MCP servers, improved tooling, and broader adoption, one thing is clear: MCP is not just solving today's integration challenges, but building the infrastructure for tomorrow's AI-powered world.
Ready to explore MCP? Start with our Complete Guide to Finding and Using MCP Servers or visit the official MCP documentation to begin your journey.
