What Is an MCP Server Essential in the Age of Generative AI? Why It’s Popular and Recommended Servers Explained
✅ This Article Is Recommended For:
- Developers and engineers looking to integrate AI agents or LLMs (Large Language Models) into their workflows
- Those who want to connect AI with tools like Slack, GitHub, and Google Drive
- Product managers and tech leads considering the implementation of an MCP server
- IT professionals aiming to automate or streamline business processes with AI
1. What Is an MCP Server? — Like a “USB-C” That Connects AI and Tools
The MCP (Model Context Protocol) server is an open standard for connecting AI models with external tools and data sources. It was announced by Anthropic in November 2024 and has since been adopted by leading AI companies such as OpenAI and Google DeepMind.
This protocol provides a common language that allows AI to interact with external applications and services safely and efficiently. For example, when an AI is asked to “fetch the latest sales report,” the MCP server translates the request into the appropriate API call and retrieves the necessary data.
2. Why Is the MCP Server Gaining Attention? — Reasons for Its Popularity
MCP servers are widely used for the following reasons:
- Unified Interface: Enables easy integration with various tools and services.
- Improved Development Efficiency: Eliminates the need for individual API integrations, reducing development time.
- Enhanced Security: Designed to require explicit user permissions, ensuring safety.
- Flexible Scalability: Easily adapts to new tools and services.
Thanks to these features, MCP servers play a key role in enhancing the practicality of AI and supporting business automation and efficiency.
3. Top 10 Recommended MCP Servers — Categorized by Use Case
Here are some of the most popular MCP servers categorized by application:
🔧 For Development and Code Management
- GitHub MCP Server: Automates repository management and code reviews with AI.
- Desktop Commander MCP: Supports file operations and command execution in local environments.
🗂️ For File and Document Management
- Google Drive MCP Server: AI can search and summarize files in Google Drive.
- File System MCP Server: Allows AI to directly read and write local files.
💬 For Communication Tool Integration
- Slack MCP Server: AI summarizes messages and automates notifications.
- Notion MCP Server: Helps AI manage notes and databases.
🌐 For Databases and Search
- PostgreSQL MCP Server: AI performs database queries and analysis.
- DuckDuckGo MCP Server: Enables AI to execute privacy-focused web searches.
🧪 For Testing and Deployment
- Puppeteer MCP Server: Automates browser operations for UI testing with AI.
- Docker MCP Server: AI manages and deploys containers.
By selecting the right server for your development environment or workflow, you can extend AI’s capabilities even further.
4. How to Implement an MCP Server — Step-by-Step Guide
Follow these steps to implement an MCP server:
- Clarify Your Goals: Define the tasks you want the AI to handle.
- Select the Right MCP Server: Choose a server that matches your needs.
- Set Up the Server: Build the server following official documentation.
- Configure Integration with AI: Grant the AI model access to the MCP server.
- Test and Verify: Run real-world workflows and adjust as necessary.
During implementation, be sure to configure proper security and access controls.
5. Security and Accessibility — For Safe and Inclusive Use
To build a secure and accessible environment when using MCP servers, consider the following:
- Explicit User Consent: Require user permission before the AI accesses tools or data.
- Access Control Settings: Limit AI access to only what is necessary to prevent unauthorized operations.
- Logging and Monitoring: Record AI activity and monitor for anomalies.
- Thoughtful UI Design: Consider users with visual or hearing impairments.
By taking these precautions, you can use MCP servers with peace of mind.
Conclusion — Expand the Possibilities of AI with MCP Servers
MCP servers are powerful tools that bridge AI with external tools and data sources, enabling business automation and efficiency. Choosing the right server and implementing it with attention to security and accessibility greatly expands the potential applications of AI.
The MCP server ecosystem is expected to grow, with new servers emerging to meet various needs and use cases. Now is the perfect time to consider implementing an MCP server.
References
- Anthropic. (2024). Introducing the Model Context Protocol.
- Wikipedia. (2025). Model Context Protocol.
- ByteByteGo. (2025). EP163: 12 MCP Servers You Can Use in 2025.
- Medium. (2025). MCP Explained: The New Standard Connecting AI to Everything.
- Digma AI. (2025). 15 Best MCP Servers for Developers in May 2025.