Both MCP servers and traditional APIs let you extend AI capabilities, but they serve different purposes. Here's when to use each.
What is Model Context Protocol?
Model Context Protocol (MCP) is a standardized way for AI assistants like Claude to interact with external tools and data sources. It's designed specifically for AI-to-tool communication.
MCP Server vs API: Key Differences
MCP Servers
Best for:
- AI assistant integrations (Claude, ChatGPT)
- Structured tool definitions
- Real-time data access
- Function calling patterns
Characteristics:
- Standardized protocol
- Built for AI consumption
- Tool/resource definitions
- SSE (Server-Sent Events) support
Traditional APIs
Best for:
- Web applications
- Mobile apps
- Third-party integrations
- REST/GraphQL patterns
Characteristics:
- Flexible endpoints
- Standard HTTP methods
- Custom authentication
- General-purpose
When to Use MCP
Use MCP when:
- Building AI tools - Your primary use case is AI assistant integration
- Structured tools - You need standardized tool definitions
- Real-time data - You need live data streaming
- Claude/ChatGPT - You're targeting these platforms specifically
When to Use Traditional APIs
Use traditional APIs when:
- Web/mobile apps - Your consumers are applications, not AI
- Flexible endpoints - You need custom endpoint structures
- Existing infrastructure - You already have REST/GraphQL APIs
- General integrations - You need broad compatibility
Can You Use Both?
Absolutely! Many successful products offer both:
- MCP server for AI assistant integration
- REST API for web/mobile apps
DeployContext supports both patterns. You can deploy an MCP server and expose a REST API from the same codebase.
Conclusion
MCP servers are purpose-built for AI assistants. If that's your primary use case, MCP is the way to go. For general-purpose integrations, traditional APIs still have their place.
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