Overview

This paper summarizes a discussion aimed at clarifying the differences and relationship between Application Programming Interfaces (APIs) and the Model Context Protocol (MCP). Our conversation progressed from defining each concept to exploring their specific functionalities and the role of authentication in their usage.[1]

What is an API?

We began by understanding an API using a simple analogy. An API is like a waiter in a restaurant. When one software application (the customer) wants to access the functionalities or data of another software application (the kitchen), it doesn't directly interact with the internal workings. Instead, it sends a request through the API (the waiter) using a defined set of rules (the menu). The API then retrieves the requested information or performs the desired action and returns the result to the requesting application. APIs are a fundamental way for diverse software systems to communicate and share information.[2]

Introducing the Model Context Protocol (MCP)

The Model Context Protocol (MCP) was introduced as a newer standard specifically designed for Artificial Intelligence (AI) models, particularly large language models (LLMs). We likened MCP to a universal USB-C adapter for AI. While APIs serve as general connection points for various software, MCP aims to provide a standardized way for AI models to seamlessly integrate and share data with external tools, systems, and data sources. It offers a consistent interface for common AI tasks like accessing files, executing functions, and handling contextual prompts.[3]

Key Differences

The core difference lies in their scope and purpose. APIs are a broad concept for inter-software communication, while MCP is a specific protocol tailored to the unique needs of AI models in interacting with external resources. Think of APIs as various types of power outlets, each with its own way of connecting, while MCP is a specialized adapter designed to provide a consistent connection for AI to a multitude of "power sources" (data and tools).[4]

The Role of API Keys and Authentication

The discussion then turned to the persistent need for API keys. We established that in 2023 and even currently, different services and AI models typically require their own API keys for authentication. MCP's focus is on standardizing the how of communication between AI and external resources, not necessarily the who in terms of identity verification. Using the restaurant analogy, even if the AI chef uses a universal order form (MCP) to request ingredients, each supplier (external data source) will still likely need to verify the chef's credentials (API key) before fulfilling the order.[5]

MCP Client and Server Architecture

To further understand MCP, we explored its client-server architecture. The MCP client is the AI model or application that needs to access external resources. The MCP server acts as an intermediary, sitting in front of the actual resource. The client sends requests to the server, which handles authorization and then interacts with the underlying resource to fulfill the request before sending the response back to the client. This division was compared to a library where the AI model is the reader (client), the desired information is the book, and the librarian (server) manages access and retrieval.[6]

MCP and Authorization

Finally, we discussed whether MCP handles authorization. We learned that the MCP specification does include authorization capabilities, often utilizing OAuth 2.1. While MCP provides a standardized framework for authorization, it doesn't mean it's entirely automatic. Developers and service providers still need to configure and implement authorization using the tools offered by MCP. This was likened to the Dewey Decimal System (MCP providing universal classification) and a library card (API key/authentication providing authorization to a specific set of libraries).[7]

Conclusion

In conclusion, APIs are a general mechanism for software communication, while MCP is a newer, specialized protocol designed to streamline the interaction of AI models with external resources through standardization. While MCP aims to simplify these interactions, the need for authentication, often through API keys or similar credentials, remains crucial for controlling access to specific services and data. MCP offers a more consistent and potentially more efficient way for AI to connect, but it works in conjunction with, rather than entirely replacing, the need for authorization.[8]

Endnotes

[1] Model Context Protocol (MCP) vs. APIs: The New Standard for AI Integration: https://medium.com/@tahirbalarabe2/model-context-protocol-mcp-vs-apis-the-new-standard-for-ai-integration-d6b9a7665ea7