The Rise of the Model Context Protocol (MCP) #
Imagine a world where AI systems are not limited by the tools they can access, but instead can seamlessly interact with a vast array of services, databases, and applications. That’s the vision behind the Model Context Protocol (MCP), a groundbreaking initiative that’s rapidly reshaping the AI developer ecosystem.
MCP was introduced by Anthropic in November 2024 as a way to make AI tools and platforms model-agnostic. It works by defining servers and clients. MCP servers are endpoints where tools and resources are defined, such as GitHub’s MCP server, which allows LLMs to read from and write to GitHub. MCP clients, on the other hand, are the connections from an AI application to MCP servers, enabling LLMs to interact with context and tools from different servers. An example of an MCP client is Claude Desktop, which allows the Claude models to interact with thousands of MCP servers.
The Power of Concentration and Innovation #
In just a short time, MCP has become the backbone of hundreds of AI pipelines and applications. Major players like Anthropic and OpenAI have integrated it into their products, while developer tools such as Cursor and productivity apps like Raycast also use MCP. Thousands of developers use it to integrate AI models and access external tools and data without having to build an entire ecosystem from scratch.
One of the most intriguing findings is the concentration of MCP server use. Despite there being thousands of MCP servers, the top 10 servers make up nearly half of all GitHub stars given to MCP servers. This concentration suggests that a few key servers are dominating the ecosystem, likely due to network effects and practical utility. Developers are gravitating toward servers that solve universal problems like web browsing, database access, and integration with widely used platforms like GitHub, Figma, and Blender.
The Three Categories Driving MCP Use #
When analyzing the types of servers that are most popular, three categories stand out: Computer & Web Automation, Software Engineering, and Database & Search. Together, they received nearly three-quarters (72.6%) of all stars on GitHub. This dominance reflects the fundamental need for AI systems to interact with web content and databases, and to support software development.
Software Engineering MCP servers are particularly popular, aligning with Anthropic’s economic index, which found that a significant portion of AI interactions are related to software development. This makes sense, as AI tools are increasingly being used to streamline and automate the development process.
The Future of MCP: Read and Write, Not Just Read #
One of the most exciting aspects of MCP is that it allows AI applications to not only access data and tools (read) but also to take action and interact with services on a user’s behalf (write). Across all but two of the MCP server categories, the most popular servers support both reading and writing operations. This means that agents are not just answering questions based on data but also taking action and interacting with services.
This capability opens up a world of possibilities. Imagine an AI agent that can not only provide information but also edit files, send emails, and automate tasks. The implications are vast, and the potential for innovation is enormous.
The Road Ahead: Openness and Interoperability #
As the MCP ecosystem continues to evolve, the importance of open APIs and fluid memory standards becomes increasingly clear. Open APIs allow MCP applications to access third-party tools for agentic use and context, while fluid memory standards ensure that the memory context accrued at OpenAI and other leading developers does not get stuck there, preventing downstream innovation.
The future of MCP is bright, and its potential to democratize AI development is immense. By addressing both technical architecture and market dynamics, MCP can achieve its potential as a democratizing force in AI development, rather than merely shifting bottlenecks from one layer to another.
Sourced from the article: MCP in Practice
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