The basics of MCP (Model Context Protocol) 2025
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments.Introducing the Model Context Protocol
Anthropic announced the Model Context Protocol (MCP) on November 26, 2024. It seems difficult to understand the MCP from the announcement. But, it’s not difficult to understand the MCP. MCP is a protocol for connecting AI assistants to the systems where data lives without reinventing the wheel. If some developers have already implemented the connector for their systems, they can connect their systems to the AI assistants without implementing the connector again.
Example
Playwright MCP is a great example.
GitHub - Playwright-MCPFrontend developers have concerns about the quality of the code generated by AI assistants. They want to make sure the code is correct and works in the browser. Playwright is a tool for testing web applications. Playwright MCP is a connector for Playwright that allows you to connect Playwright to the AI assistants.
You can use Playwright MCP following the steps below:
Run the Playwright MCP server.
npx @playwright/mcp@latest --port 8931Command LLM to generate the test code for the web application.
For example, I use Cursor to generate the test code.
Here is an example of the prompt generating tests for the web application:
ゴール
- Playwright用のE2Eテストの作成
作成するテスト
- /writing/mcp-2025 に対するテスト
テスト作成方法
- PlaywrightのMCPを使ってローカルの開発環境にアクセスして要素を解析してください。そして、解析した要素をベースにE2Eテストを作成してください。Run the test code.
Key Benefits of MCP
Most technology protocols claim to solve integration problems, but few deliver. MCP stands apart as a zero-to-one innovation in AI system connectivity.
Vertical Integration
While others endlessly rebuild connectors, MCP enables true vertical integration. Developers can finally escape the redundant cycle of implementing the same connections across different systems. This isn’t incremental improvement—it’s a step-function change in deployment efficiency.
Competitive Focus
The winners in AI won’t be companies distracted by infrastructure problems. By delegating integration work to MCP, model developers can deploy their talent where it matters: creating superior AI capabilities that actually differentiate their product in the market.
Monopolistic Advantage
The centralization that MCP enables isn’t just convenient—it creates network effects. As services converge through a single gateway, the value of each additional connection increases exponentially. Early adopters will build moats through unified access control and comprehensive logging that competitors will struggle to replicate.
Challenges and Considerations
The path to adoption isn’t without obstacles. Those who recognize these challenges early will capitalize on the opportunity.
Configuration Complexity
The current configuration landscape for MCP resembles the early internet—powerful but unnecessarily complex. The first company to create an intuitive management interface will dominate this space, turning a technical barrier into a market advantage.
Security Paradigms
Security isn’t a feature—it’s binary. Either your system is secure or it isn’t. Since MCP connectors directly interface with private data, the winners will be those who treat security as a foundational principle rather than an afterthought. This isn’t about following best practices—it’s about establishing them.
Future Directions
The future belongs to those who can see it clearly. MCP’s trajectory will reshape AI integration in ways few currently recognize.
Definitive Industry Standard
AI adoption will bifurcate into two camps:
Winners
Those using MCP
Losers
Those rapidly becoming irrelevant
As organizations integrate AI deeper into their workflows, standardizing on MCP won’t be optional—it will determine which companies can scale their AI capabilities and which cannot.
Connector Monopolies
The next generation of tech giants will emerge from those who build the most essential MCP connectors. Real-time data processing, dynamic tool selection, and autonomous system management aren’t just features—they’re the foundation of new technological monopolies.
Ecosystem Dominance
The MCP Router will evolve from infrastructure to platform. The companies that control this layer will effectively control access to the entire AI ecosystem, creating unprecedented power dynamics in the technology landscape.
Conclusion
MCP represents a fundamental shift in how we connect AI with data systems. Most will miss its significance, viewing it as merely another integration protocol rather than a complete paradigm shift in AI utility.
The challenges of implementation aren’t bugs—they’re features that will separate visionary early adopters from followers. Those who solve these challenges first will establish insurmountable advantages.
Within five years, MCP will become the invisible infrastructure powering the AI economy. The question isn’t whether organizations will adopt MCP, but whether they’ll do so as leaders or laggards. The former will capture disproportionate value; the latter will merely exist in a market defined by others.
Organizations with foresight will recognize that MCP isn’t just about technical connectivity—it’s about creating defensible positions in the AI-driven future that’s already arrived.