Understanding MCP: A New Standard for AI Integration
The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI systems to connect with real-world tools and data sources. Announced on November 25, 2024, MCP acts as a universal bridge between AI models like Claude and external systems such as databases, APIs, and business tools. You can read the official announcement for more details and access to the GitHub repository.
Arjan Codes recently published an excellent practical demonstration of MCP implementation. Having completed his software architecture course, I appreciate how Arjan delivers clear, practical content from an independent perspective - refreshingly not tied to major cloud providers, which is common in architecture discussions.
Architectural approaches for MCP implementation
When implementing MCP, developers face key architectural decisions:
| Approach | Direct Module Access | API Layer Integration |
|---|---|---|
| Implementation | MCP server calls internal modules directly | MCP server interacts with existing REST API |
| Best for | Small-scale projects | Systems with established APIs |
| Advantages | Simpler setup | Reuses existing logic, avoids duplication |
| Limitations | Tight coupling, scales poorly | Extra network hop, added complexity |
Overall, Arjan shares these conclusions:
- Direct access works well for prototypes but becomes difficult to maintain as systems grow
- API integration leverages existing infrastructure but adds communication overhead
- Auto-conversion (using tools like
fast_mcp) can generate MCP servers from FastAPI applications
For concrete coding examples of these approaches, I recommend watching Arjan’s full video demonstration.
I believe MCP represents an important evolution - soon, even internal company tools might include AI interfaces as standard features, fundamentally changing how we interact with software.
