MCP The Birth of an Open AI Ecosystem.pdf
- 1. MCP is gaining massive traction. With thousands of MCP “servers” now in operation, what started as an innovation by Anthropic has been embraced even by OpenAI. This marks the beginning of a true AI ecosystem, much like how mobile apps revolutionized technology a decade ago. What is MCP and Why is it a Game-Changer? The Basics MCP, or Model Context Protocol, was introduced by Anthropic as an open standard in November 2024. While the initial response was tepid, adoption skyrocketed in early 2025. A defining moment came in March when even OpenAI, Anthropic’s key competitor, adopted it. At its core, MCP allows AI models to extend their capabilities much like apps extend the functionality of smartphones. But instead of standalone applications, MCP creates a network of interconnected AI extensions — known as MCP servers — that can seamlessly work together across different platforms.
- 2. The Structure of MCP To understand MCP, there are two main components to focus on: MCP Hosts — Applications like Claude Desktop that can connect to multiple MCP servers. MCP Servers — External services that expand the AI’s capabilities. (There’s also the notion of MCP Clients, but for this discussion, hosts and clients can be considered synonymous.) The beauty of MCP lies in its open standard approach, meaning that a single MCP server can work across multiple hosts. A growing number of host applications already exist — such as Claude Code, Cursor, and oterm — while thousands of servers are being developed, cataloged on sites like mcp.so. Real-World Examples of MCP Servers Anthropic provided a reference suite of MCP servers to showcase the power of the ecosystem: Google Maps — Fetches local search results and place details. Slack — Sends and receives messages. Memory — Stores and recalls information across sessions. Time — Handles time and timezone conversions.
- 3. Puppeteer — Interacts with headless browsers to fetch HTML and images. EverArt — Generates images, proving that MCP isn’t limited to text. The rapid growth is astounding — from launch to 5,000 applications in just months — highlighting MCP’s potential to revolutionize AI integrations. Why MCP Matters The Emergence of an AI Ecosystem MCP servers function as the first true AI-native apps, but with key advantages over traditional app ecosystems. Unlike APIs, which are rigid and developer-dependent, MCP is dynamic and user- friendly, with text-based inputs and outputs making integration seamless. A Unified Standard for AI With OpenAI and Anthropic both on board, MCP could avoid the fragmentation seen in mobile platforms (Android vs. iOS). This standardization means developers can implement MCP once and instantly make their services available across dozens of host applications.
- 4. Write once, use everywhere. Whether users are on Claude, Cline, or Gemini, they can access the same MCP servers. Power of Integration & Chaining Unlike standalone apps that require custom integrations (or tools like Zapier), MCP allows AI hosts to combine results from multiple servers, creating powerful AI workflows. For example, imagine a Slack-based dinner reservation system: A user says, “Find us a place to eat tonight.” The host queries Google Maps and Yelp MCP servers for suggestions. It references Memory MCP to consider users’ past preferences. It finalizes the booking using the OpenTable MCP server. It posts in Slack: “I’ve checked your preferences and made a reservation at X.” This kind of automated, multi-step process is a superpower unique to MCP. A Step Toward AI Mesh Networks AI agents can be both hosts and servers, making them interdependent and collaborative. For instance:
- 5. Claude Code can act as a host by using GitHub MCP to check in code. But Claude Code can also function as a server, responding to requests from Claude Desktop for coding assistance. This lays the foundation for a network of AI agents that communicate fluidly, unlocking new levels of efficiency and problem-solving. Is MCP Just Another Developer Tool? Not Quite. MCP might sound similar to traditional API-based tools, but there are two major differences: Designed for Users, Not Just Developers — Unlike APIs that require structured developer implementation, MCP is meant to be dynamic and user-controlled. Customizable Toolsets — Each user can curate their own MCP tool collection, adding or removing servers as needed. A Simple Yet Powerful Use Case One of the easiest ways to leverage MCP is through a personalized daily news bulletin. Using only the Memory MCP server, here’s how a user can build an AI-powered news digest: 1. The user stores preferences (e.g., “I’m interested in AI, technology, and global news”).
- 6. 2. The AI fetches the latest news and summarizes it. 3. To avoid repetition, it remembers what was shown yesterday. 4. The user can then integrate additional MCP servers: Google Tasks to add follow-ups based on the news. Calendar to incorporate relevant events. Email or Slack to share key insights. Before MCP, setting this up required building a full-fledged app, a database, and multiple API integrations. Now, it can all be done through an AI assistant using natural language. How MCP Affects You If you work with AI systems, now is the time to think about your role in this growing ecosystem. Ask yourself: Should I expose my system’s capabilities as an MCP server? Can I provide users with a new way to interact with my AI system? Do I even need a traditional user interface anymore, or can any MCP host serve as my UI? Would turning my application into an MCP host enhance its potential?
- 7. Personally, I am already planning to incorporate MCP into my open- source work on Islamic AI assistants, developing both the server and host functionalities. What’s Next for MCP? Challenges to Overcome Installation & Setup — Currently, adding an MCP server requires editing JSON files and running Docker or Node.js locally — far from user-friendly. Security & Authentication — Setting up authentication, like Google Drive API keys, is still cumbersome. Prompt injection and permission handling need improvement. Dynamic Discovery — Standardizing how LLMs discover and access MCP servers will be crucial for broader adoption. Conclusion: A New Era of AI While MCP may seem abstract or technical, it represents a massive leap forward — the first true open AI ecosystem. It enables AI to not only extend its own capabilities but also chain multiple services together, opening up endless possibilities. With thousands of MCP servers already live and growing, the question isn’t whether MCP will take off — it’s how you will participate in it.