MCP vs RAG: What's the Difference and When Should You Use Each?

Two acronyms dominate the conversation about giving AI agents access to external knowledge: MCP and RAG. They get conflated constantly, used interchangeably in blog posts, and occasionally confused even by developers actively building with them.

They're not interchangeable. MCP and RAG solve different problems at different layers of the stack. Understanding the distinction is practical, not academic — it determines what you build, what you buy, and where things break in production.

What RAG is

RAG — Retrieval-Augmented Generation — is a technique for grounding language model responses in external content. The core pattern is:

1. Convert a corpus of documents into vector embeddings and store them in a database
2. When a query arrives, embed the query and find the most semantically similar stored chunks
3. Inject those chunks into the LLM's context window as retrieved context
4. Ask the LLM to answer using that context

RAG is fundamentally about what an agent knows. It's the pipeline that takes a question and retrieves relevant content to answer it. The retrieval is semantic — not keyword matching — which is why it works well even when the user's phrasing doesn't match the documentation verbatim.

When someone says "add RAG to your app" they mean: build a system that retrieves relevant chunks of your content and injects them into the LLM's prompt. The LLM then generates an answer grounded in those chunks rather than its general training knowledge.

What MCP is

MCP — the Model Context Protocol — is a JSON-RPC 2.0 protocol for how AI agents connect to tools and services. It defines the wire format: how an agent discovers available tools, calls them, and interprets the results.

MCP is fundamentally about how an agent interacts with the world. It's the transport layer — the standardized interface between the LLM and any capability you want to expose to it.

An MCP server exposes tools. A tool has a name, a description, and an input schema. The agent decides which tools to call and with what arguments. The MCP server executes the tool and returns the result. The agent incorporates that result into its reasoning.

MCP doesn't specify how tools work internally. A tool could hit a live database, call a REST API, run a shell command, or — yes — run a RAG query against a vector index.

The relationship between them

MCP and RAG operate at different layers and are not alternatives to each other — they compose.

An MCP server can expose a RAG-backed tool. When an agent calls ask_site on AgentReady's MCP server, the server runs a RAG query against its vector index and returns the answer. From the agent's perspective, it called a tool. Under the hood, that tool ran semantic retrieval against embedded chunks from the indexed site.

The simplest way to think about it: MCP is the API; RAG is the implementation of one of the endpoints on that API.

What each one solves

RAG solves the knowledge problem. LLMs have a training cutoff and fixed context windows. They don't know about your latest product changelog, your internal documentation, or anything that happened after their training data was collected. RAG lets you inject relevant up-to-date content into the prompt at query time, so the LLM can answer questions about your specific domain with sourced, current information.

MCP solves the integration problem. Before MCP, every AI tool had to write custom adapters for every application that wanted to use it. Cursor used one protocol, Claude Desktop used another, VS Code extensions used a third. MCP standardizes this so a tool built once works everywhere that speaks MCP. You write the server once and it connects to any MCP client — Claude Desktop, Cursor, Continue.dev, or a custom agent.

When you need RAG

RAG is the right choice when the question is: "How do I give this LLM access to a body of content it doesn't already know?"

• Your documentation is too large to fit in a context window
• You need answers grounded in specific, current content rather than general knowledge
• You need source citations so users can verify answers
• The content changes over time and the agent needs to stay current
• You're querying across many pages and need semantic search to find the relevant ones

RAG is not the right choice for live queries to external APIs, taking actions, or anything that requires real-time data that can't be pre-indexed. For those, you want direct tool calls.

When you need MCP

MCP is the right choice when the question is: "How do I expose capabilities to AI agents in a standardized way?"

• You want your tool to work with Claude Desktop, Cursor, and any future MCP clients without rewriting integrations
• You're building a service that agents should be able to discover and call
• You want agents to take actions — not just retrieve information
• You're building an agent pipeline and want a standard way to compose capabilities

MCP on its own doesn't give the agent knowledge — it gives the agent a way to call tools. What those tools do is up to you.

A concrete example

Suppose you want AI agents to be able to answer questions about your API documentation. Here's how the two technologies work together:

RAG component: Your documentation is crawled, chunked, and embedded into a vector database. When a question arrives, the system retrieves the top-k most relevant chunks, injects them into an LLM prompt, and generates a cited answer.

MCP component: An MCP server exposes this as a tool called ask_site. The tool has a JSON schema: it accepts domain and query. An agent using Claude Desktop adds the MCP server to its config and can now call ask_site from any conversation.

Without RAG, the MCP tool would have nothing to retrieve. Without MCP, the RAG pipeline has no standardized way for agents to call it. They're complementary.

The alternative to RAG for documentation

The most common alternative to RAG for giving agents documentation access is raw web fetching — the agent just requests the URL directly. Tools like web_fetch in Claude and WebFetchTool in various agent frameworks do this.

Web fetching has three limitations that RAG addresses:

1. JS-rendered sites return empty. If your documentation uses React, Next.js, or any SPA framework, raw HTTP fetching gets the shell — not the content. RAG indexes the rendered output.

2. Single-page scope. A web fetch gets one page. If the answer spans multiple pages, the agent has to make multiple requests and hope it fetched the right ones. RAG searches across all indexed pages in a single query.

3. Context window limits. A long documentation page may exceed the context window. RAG retrieves only the relevant chunks, so the agent gets exactly what it needs without burning tokens on the rest.

Our benchmark of 37 developer sites found that indexed RAG produces significantly fewer high-confidence wrong answers than web fetching. The gap is largest for JS-heavy sites and questions that span multiple pages.

The quick reference

What AgentReady provides

AgentReady is both: a RAG pipeline that indexes any website, exposed via an MCP server. You submit a URL, the site gets crawled, rendered, chunked, and embedded. That index is then available as a tool on an MCP endpoint that any MCP client can connect to.

The ask_site tool runs a RAG query. The refresh_site tool re-crawls to update the index. The submit_site tool triggers the initial crawl. All exposed over MCP so they work in Claude Desktop, Cursor, or any other MCP client without additional setup.