Model Context Protocol (MCP) First vs API First: Choosing the Right Approach for AI-Driven Platform Engineering

Model Context Protocol (MCP)

Introduction: The Evolution of AI Platform Integration

As AI-driven platforms mature, their integration with external systems, tools, and data sources becomes increasingly complex. Two prominent paradigms have emerged to address this challenge—traditional API First approaches, and the newly introduced Model Context Protocol (MCP) First approach. Understanding the strengths and roles of each is essential for building future-proof, scalable AI platforms.

Defining the Players: What are API First and MCP First?

API First Approach

The API First approach prioritizes designing and exposing well-defined application programming interfaces (APIs) before building the underlying services. APIs act as contracts enabling external applications to interact with AI platforms, allowing:

·       Standardized, deterministic machine-to-machine communication

·       Clear integration points for third-party developers and systems

·       Mature ecosystems with broad adoption and tooling support

APIs typically expose discrete functionality, operate statelessly, and require explicit programming for interaction.

Model Context Protocol (MCP) First Approach

MCP, introduced by Anthropic in late 2024, is an open protocol standard specifically designed for AI-human-tool interaction and context management. It enables AI models, especially large language models (LLMs), to interface seamlessly with external tools, data sources, and functions in a contextual, dynamic manner by:

·       Providing a standardized, self-describing interface that allows AI agents to discover, reason about, and use external resources autonomously

·       Supporting context-rich communication to maintain state and session coherence across interactions

·       Reducing integration complexity by abstracting diverse APIs and data sources into uniform MCP servers

·       Enabling flexible orchestration of tool use and context sharing within AI-driven workflows

Concepts of MCP (Model Context Protocol)

The Model Context Protocol (MCP) uses a client–server architecture where an MCP host, such as an AI application like Claude Code, Perplexity Labs or JupyterLab with AI extensions, connects to one or more MCP servers.

For each MCP server it connects to, the host creates a dedicated MCP client that maintains a one-to-one connection with that server. This design keeps connections isolated and allows the host to manage multiple servers simultaneously with ease.

Key Participants

1.       MCP HostThe AI application orchestrating workflows and managing multiple MCP clients.Example: Perplexity Labs acting as the host.

2.       MCP ClientThe component responsible for maintaining a dedicated connection to a specific MCP server, retrieving contextual information for the host.Example: The JupyterLab AI extension instantiates an MCP client for each connected server.

3.       MCP ServerA program or service providing contextual data and services to MCP clients.Example: A LangChain MCP server connecting to knowledge bases or a Hugging Face MCP server serving AI model pipelines.

Example Flow

When Perplexity Labs (the MCP host) connects to a LangChain MCP server, it launches an MCP client dedicated to that connection. Similarly, if Perplexity Labs also connects to a Hugging Face MCP server, it creates a separate MCP client for that server.

This structure allows a single host application to maintain independent, parallel interactions with multiple MCP servers, enhancing modularity and enabling scalable AI integrations.

Ref : https://modelcontextprotocol.io/docs/learn/architecture#concepts-of-mcp

Key Differences: Model Context Protocol vs API First

Aspect	API First	Model Context Protocol (MCP) First
Core Purpose	Define fixed endpoints for system functionalities	Enable AI models to dynamically interact with tools and context
Interaction Style	Predefined, stateless API calls	Context-aware, adaptive, and session-based interactions
Integration Complexity	High effort with many bespoke connectors	Simplifies connectors using common protocol for multiple sources
Context Handling	Generally limited; requires external orchestration	Built-in context management and state awareness
Adaptability	Changes can break clients	Self-describing tools allow AI to adapt automatically
Scalability	Scales through robust API design and infrastructure	Scales by managing multi-tool orchestration and data sources efficiently
Suitability for AI Platforms	Excellent for exposing services and inference endpoints	Essential for seamless AI agent-tool integration with contextual memory
Relationship to APIs	The foundation for exposing functionality	Consumers and harmonizers of APIs for AI-driven workflows

Why MCP First Matters for AI-Driven Platforms

AI platforms powered by large language models require more than just API endpoints—they need dynamic, context-aware, and goal-driven interactions with diverse tools, datasets, and services. MCP addresses these needs by:

·       Acting as a universal "language" and protocol that unifies fragmented APIs into a consistent interface

·       Allowing AI agents to discover available tools, understand their capabilities, and orchestrate usage without manual glue code

·       Providing persistent context and memory, crucial for complex reasoning and multi-step tasks

·       Enabling developers to build AI-native applications that interact smoothly with enterprise systems, databases, and user environments

Harmonizing MCP and API First: Not a Choice, But a Necessity

MCP is not a replacement for APIs; rather, it consumes and enhances APIs by presenting them in an AI-friendly, context-rich format. The most robust AI platforms leverage:

·       API First design for well-defined, secure, and scalable service endpoints

·       MCP First principles to provide AI agents with a seamless, integrated, and context-aware toolset

This synergy allows platforms to scale not just in terms of compute, but also in intelligent, flexible AI-human-tool collaboration.

Choosing the Right Approach

For traditional software applications, API First remains indispensable. However, as AI capabilities expand, Model Context Protocol First represents the next evolution, enabling platforms to provide rich contextual experiences and intelligent orchestration of tools.

Frugal Scientific’s vision of next-gen AI platforms embraces both: architecting APIs as the solid functional backbone while empowering AI agents with MCP to unlock true context-aware intelligence and seamless integration.