A practical guide to Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP), what they are, how they differ, and how they work together with examples.
Modern AI systems often face two big challenges:
Two concepts are at the center of these solutions: Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP).
They are related but solve different problems. Understanding their roles helps clarify how today’s AI agents are built.
Retrieval-Augmented Generation (RAG) is a technique that improves an AI’s answers by retrieving relevant information from an external knowledge base, such as documents, databases, or APIs, before generating a response.
This allows the AI to move beyond the static training data it was built on, reducing hallucinations and providing up-to-date, context-rich answers.
LLMs are powerful, but they can only respond based on what they were trained on. With RAG, you can connect them to your own data sources: company policies, research papers, or customer documentation. This makes them more useful in real-world scenarios.
Model Context Protocol (MCP) is a standardized communication protocol that defines how AI applications (clients) can interact with external services (servers).
It is not a model or a technique, but a set of rules. MCP uses JSON-RPC (2.0) for handling requests and responses, which means every interaction between an LLM (client) and an MCP server follows a structured JSON format for making remote procedure calls.
Example JSON-RPC 2.0 request and response:
// Request: Claude (MCP client) asking GitHub MCP server to create a pull request
{
"jsonrpc": "2.0",
"id": 1,
"method": "create_pull_request",
"params": {
"repo": "fwwz/awesome-app",
"branch": "feature-x",
"title": "Add new feature"
}
}
// Response: GitHub MCP server returns the result
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"url": "https://github.com/fwwz/awesome-app/pull/42",
"status": "success"
}
}In other words:
| Feature | RAG (Retrieval-Augmented Generation) | MCP (Model Context Protocol) |
|---|---|---|
| Core Concept | A technique to ground LLM responses in external data. | A protocol for connecting LLMs to external tools, APIs, or data sources. |
| Primary Goal | Improve accuracy and reduce hallucinations. | Provide a safe, extensible, standardized bridge between LLMs and external systems. |
| How It Works | 1. Retrieve relevant documents from a knowledge base. 2. Add them to the model’s context. 3. Generate a grounded response. | 1. LLM (client) sends a JSON-RPC request. 2. MCP server calls the underlying service or API. 3. Server returns result to the client. |
| Analogy | A student writing a report: go to the library, read references, then write. | A universal power adapter: safely connect to any outlet (tool or API). |
| Relationship | RAG is a pattern that may use MCP as its foundation. | MCP is a framework that can enable RAG and many other applications. |
Imagine an AI assistant that answers questions about your company’s documentation.
Without MCP
You would write custom code connecting the AI directly to a vector database. This is brittle, hard to maintain, and poses security risks.
With MCP
User: "What is our vacation policy?"
Claude (MCP client):
→ sends request { "action": "search_docs", "params": {"query": "vacation policy"} }
MCP Documentation Server:
→ queries internal knowledge base
→ returns relevant HR policy document
Claude:
→ uses returned context to generate a grounded answerThis is a RAG system built on top of MCP.
MCP is not limited to RAG. For example, if you want Claude to open a pull request on GitHub:
Claude (MCP client):
→ sends request { "action": "create_pull_request", "params": {
"repo": "fwwz/awesome-app",
"branch": "feature-x",
"title": "Add new feature"
}}
MCP GitHub Server:
→ translates request into GitHub REST API call
→ authenticates securely
→ creates pull request
→ returns PR link
Claude:
→ responds with "I have opened a pull request: <link>"Instead of coding custom integrations for each LLM, MCP provides a standardized interface that any compatible model can use. For example, once you have a GitHub MCP server, Claude, GPT-4, or any other MCP-enabled LLM can interact with GitHub through the same endpoint.
A practical demonstration of this can be found in this Reddit post, where Claude seamlessly interacts with GitHub's API through MCP.
Alternatively, you could use a local model with Ollama. This article provides a detailed step-by-step guide on how to connect Ollama with an MCP server.
This standardization showcases two key benefits of MCP:
MCP servers can expose many different tools:
By abstracting away API differences, MCP allows AI agents to operate more like advanced automation scripts that can think.
RAG and MCP are not competing ideas, they complement each other.
As AI agents evolve, expect MCP to play a growing role in connecting LLMs to the real world, with RAG as one of its most important applications.