From RPC to MCP: The Evolution of Server Models and Context Protocols

Have you ever stopped to think about how your computer or phone actually talks to the gigantic servers that power the internet? It’s not magic, but a series of incredibly smart rulebooks called protocols. These protocols dictate how information is packaged, sent, and understood between different machines.

For decades, the workhorse of this digital conversation has been the Remote Procedure Call (RPC). It was a brilliant, simple idea that let one computer tell another to run a piece of code, just like calling a local function.

But the world has changed. The rise of sophisticated AI agents and Large Language Models (LLMs) demands a new, more nuanced way to communicate. It’s no longer just about running a function; it’s about providing context. This need for rich, shared, and dynamic context is what has led us to the next stage: the Model Context Protocol (MCP).

Let’s take a journey through this evolution, looking at how server models changed from simple, one-off calls to the context-aware, stateful conversations we need for the age of AI.

The Era of Simple Calls: The RPC Blueprint (1980s – Early 2000s)

The idea of the Remote Procedure Call (RPC) first gained traction in the early 1980s. The goal was elegant simplicity: make talking to a program on another computer feel exactly like calling a local function.

Imagine you’re baking a cake, and you need to ask a friend for a cup of sugar. A local function call is like opening your pantry. An RPC call is like calling your neighbor. You don’t care how the sugar gets to you, just that you ask for it and get it back.

How RPC Worked (And Still Works)

1. Stubs and Marshalling: The system used “stubs” small bits of code on both the client and server. The client stub would take your function request and its parameters, and “package” them up into a network message. This packaging is called marshalling.
2. The Trip: The message traveled over the network to the server.
3. Unmarshalling and Execution: The server stub would unpack the message (unmarshalling), turn the data back into the original parameters, and then call the actual function on the server.
4. Reply: The result was packaged up and sent back to the client.

Key Features of Classic RPC:

• Focus: Procedure execution. “Do this specific thing and give me the result.”
• Transparency: Hiding the network details from the programmer.
• Protocol Agnostic: Could run over various network protocols (like TCP/IP).
• Examples: Early Sun RPC, DCE/RPC.

RPC laid the foundation for distributed computing. It was robust, but it had a significant limitation: it was generally synchronous (the client had to wait for the server to finish) and stateless (each call was separate; the server generally didn’t remember what happened in the previous call without extra effort).

The Web’s Revolution: REST and the Stateless Resource Model (Late 1990s – Today)

As the internet exploded, a new architectural style called Representational State Transfer (REST) became the dominant server model. While not a direct RPC replacement, it represented a fundamental shift in how we think about server interaction.

REST wasn’t about calling a procedure; it was about managing resources. Instead of run_order_placement(user_id, item_id), a client would simply POST /orders with a JSON payload of the order details.

The Power of Statelessness

The biggest philosophical difference with REST was its emphasis on statelessness. Each request from a client to a server must contain all the information needed to understand the request.

• Benefit: This is the key to massive scalability. Any server in a large farm can handle any request, which makes load balancing and fault tolerance simple. If one server goes down, another can immediately pick up the next request without losing any context.
• Limitation: It makes complex, multi-step workflows difficult. The client has to constantly manage and resend the necessary context (e.g., user session tokens, or a long list of items in a shopping cart) with every single request.

The Microservice Refinement: gRPC and Modern High-Speed RPC (Mid-2010s – Today)

In the microservices era, where hundreds of small services talk to each other extremely fast, the simplicity of the original RPC model made a comeback. gRPC (developed by Google) brought the best of both worlds.

It used the fundamental RPC model of procedure calls but combined it with modern performance and design:

1. Protocol Buffers (Protobuf): A super-efficient, language-agnostic way to serialize (package) data, making network transfer much faster than JSON.
2. HTTP/2: It runs on HTTP/2, enabling features like bidirectional streaming the client and server can send data back and forth simultaneously over one long-lived connection.

gRPC is fantastic for high-speed, structured communication between services, but it’s still fundamentally focused on executing a defined procedure with defined inputs and outputs. It knows what to do, but it’s not inherently built to manage a flowing, evolving context.

The Next Stage: Context is King with MCP (The AI Era)

Now, we arrive at the need for the Model Context Protocol (MCP). This evolution isn’t just about faster calls or better data formatting; it’s a paradigm shift driven by the demands of Artificial Intelligence.

Modern LLMs are not just calling a function; they are acting as agents that need to understand a vast and evolving “world state” to make decisions. They need to know:

• What files are available right now?
• What is the user’s current project directory?
• What tools can I use, and what state are they in?

If a large language model is trying to solve a problem say, “Summarize the latest 5 commits in this Git repository and post the summary to the dev-channel on Slack” it needs a shared, standardized way to discover, interact with, and get context from both the Git and Slack systems.

How MCP Fits as the Next Stage

MCP, often built on top of the underlying JSON-RPC protocol, is designed specifically to handle this “context” problem. It elevates the conversation from “What procedure should I call?” to “What is the full state of the world I can interact with?”

Feature	RPC/gRPC/REST Focus	MCP (Model Context Protocol) Focus
Primary Goal	Executing a function or accessing a resource.	Providing dynamic context and enabling tool use for an AI agent.
Data Flow	Request/Response (structured data).	Request/Response, plus dynamic capability negotiation and streaming context/notifications.
Core Abstraction	Procedure (a function call) or Resource (a noun/URL).	Tool, Resource, and Prompt (everything the AI can interact with).
Standardization	Interface Definition Language (IDL) for functions/types.	A standardized interface for Tool Discovery and Context Schema.
Tool Integration	Custom API wrappers for every service.	Unified, plug-and-play framework for all external systems.

MCP is, therefore, the protocol for the AI-driven world. It separates the “intelligence” (the LLM client) from the “capabilities” (the MCP server).

1. Discovery: The AI client connects to the MCP server, and the server automatically tells the client what tools and resources it has available (list_tools). This is dynamic tool discovery.
2. Context-Aware Calls: When the AI decides to use a tool (e.g., git/get_commits), the server can provide that data in a standardized way. The entire interaction is framed within a session that maintains state and context for the AI.
3. Standardized Access: Regardless of whether the tool is a PostgreSQL database, a Slack API, or a local file system, the AI communicates with it using the same MCP language. The MCP server handles the “translation” to the specific underlying system.

This dramatically simplifies the life of the AI developer. They no longer have to build a custom integration for every tool and every LLM. They just build one MCP server for a tool (like a file system), and any MCP-compatible LLM can use it immediately.

The Road Ahead: From Function Calls to Agentic Conversations

The timeline shows a clear progression in server models, driven by the needs of the applications we build:

1. RPC (1980s): Solved the problem of “How do I run a distant piece of code?” (The start of distributed computing).
2. REST (Late 90s): Solved the problem of “How do I create a massively scalable, web-based system to manage resources?” (The internet scale model).
3. gRPC (2010s): Solved the problem of “How do I make high-speed, internal server-to-server calls more efficient?” (The microservices model).
4. MCP (The AI Era): Solves the problem of “How does an AI agent dynamically discover and securely interact with external tools while maintaining conversational and environmental context?” (The Agentic Model).

The jump from RPC to MCP is the final major step: a move from simple, transactional calls to rich, contextual conversations. It’s the infrastructure that enables AI agents to truly become useful, capable entities that can safely and effectively interact with the complex systems that run our world.