What Is an MCP Server? Full Explanation of Differences from RAG, Benefits, and How to Use It [2025 Edition]
Introduction
As the use of generative AI continues to evolve, a new concept called the MCP Server (Model Context Protocol Server) is gaining attention. MCP enables safe integration with external data sources, dramatically enhancing AI capabilities. Unlike traditional RAG (Retrieval-Augmented Generation), MCP offers a more flexible and controlled approach.
This article provides a detailed overview of MCP servers, how they differ from RAG, the benefits of adopting them, practical usage, and the various types of MCP servers. It’s especially useful for IT departments, AI application developers, and public sector data integration professionals.
What Is an MCP Server?
An MCP server is a server that supports the Model Context Protocol, designed by OpenAI, which allows generative AI to securely and efficiently interact with external systems in real time.
Key roles of the MCP server include:
- Acts as a data provider to the AI model
- Enables access to external data through predefined methods
- Communicates via JSON-RPC for secure and structured data exchange
The core concept of MCP is to extend AI models as agents, simplifying complex operations and API integrations.
Differences Between MCP and RAG
MCP is often compared to RAG (Retrieval-Augmented Generation), which retrieves external documents to enhance AI responses. The table below highlights key differences:
| Feature | MCP Server | RAG |
|---|---|---|
| Data Access Method | Structured API calls (defined methods) | Unstructured search (via search engines) |
| Real-Time Capability | High (instant function call response) | Moderate (depends on search retrieval) |
| Security | High (explicit function definitions) | Lower (risk of information leakage) |
| Flexibility | Very high (API can be fully controlled) | Limited (depends on search results) |
| Typical Use Cases | Internal systems, finance, healthcare, IoT | FAQ generation, knowledge retrieval |
While RAG excels in natural language processing and general-purpose use, MCP is ideal for scenarios requiring accuracy and control.
Benefits of Adopting MCP
MCP offers the following key advantages:
- Enhanced Security: Limits functions AI can access, preventing unauthorized actions
- Maintainability: Clearly defined API specs simplify change management
- Extensibility: Easy to add new functions and connect with SaaS platforms
- Reduced Operational Cost: Accurate data reduces need for retraining or fixing hallucinated answers
- Compliance Support: Enables easy access control, logging, and audit tracking
Examples include:
- A financial institution connecting AI to internal accounting systems
- A healthcare provider reading patient data from electronic medical records
How to Use MCP in Practice
To implement MCP, follow these steps:
- Build an API Server: Prepare a backend with the necessary functionality
- Configure the MCP Server: Create a config file following OpenAI’s specifications
- Define Methods: Clearly describe available functions and endpoints
- Call from the Model Side: Use an OpenAI API key to access the MCP server
- Return Structured Responses: Let the AI model process and use the returned data
Example: An MCP integrated with a company’s scheduler could provide functions such as fetch events, schedule meetings, and send reminders, enabling AI to act like a personal assistant.
Types of MCP Servers
Currently, MCP servers come in the following types:
-
Open Source MCP (e.g., OpenMCP Server)
- Host it in-house for full security control
- Supports multiple languages like Python and Node.js
-
SaaS-Based MCP (e.g., Acrylic MCP, Supabase integration)
- Easily deployed in the cloud
- Requires no maintenance, ideal for smaller projects
-
Hybrid MCP
- Combines in-house and cloud features
- Offers flexible deployment, popular among large enterprises
Additionally, GitHub hosts many “OpenAI MCP examples,” providing industry-specific templates to simplify implementation and reduce the learning curve.
Conclusion
MCP servers are a key technology for secure, efficient data integration in the AI era. They offer strong value for DX (digital transformation) leaders, product managers working on automation, and those involved in public sector data governance.
By understanding the strengths and differences between MCP and RAG, organizations can fully leverage the potential of generative AI. Now is the time to explore secure and intelligent AI integration with MCP.