1. Model Context Protocol (MCP)
vs. APIs
Simplifying AI Agent Integration with
External Data
Uma Desu, GenAI Pioneer
2. Overview
• • AI agents need standardized access to
external data sources.
• • Traditional REST APIs require code changes
for new endpoints.
• • MCP, introduced by Anthropic, offers a
unified protocol for LLM tooling.
3. MCP Architecture
• • Host-client model using JSON-RPC 2.0
• • Clients discover MCP servers dynamically.
• • Primitives: tools (actions), resources (read-
only data), prompt templates
• • Enables LLMs to query and invoke functions
at runtime.
4. MCP vs. REST APIs
• • Dynamic Discovery vs. Static Endpoints
• • Runtime adaptability without redeployment
• • AI-focused primitives vs. general-purpose
calls
• • Machine-readable function catalogs for
seamless integration
5. Key Features & Advantages
• • Auto-update of available capabilities
• • Consistent interface across services
• • Reduced integration overhead for AI
applications
• • Enhanced developer experience with tool
registries
6. Use Cases
• • File system navigation and data retrieval
• • Web search and API chaining
• • Geospatial queries via map services
• • Enterprise database access and analytics
7. Ecosystem & Adoption
• • Anthropic's MCP standard, emerging from
late 2024.
• • Integration with existing API backends under
the hood
• • Growing support in LLM frameworks and
agent platforms
• • Complementary to REST APIs for broader
compatibility
8. Conclusion & Next Steps
• • MCP streamlines AI agent access to dynamic
capabilities.
• • Encouraged to adopt MCP for new AI
integrations.
• • Update curricula to cover MCP alongside
REST API design.
• • Prepare students to build next-gen agentic
systems.
