RAG vs MCP: A Guide to Native AI Apps

In AI systems, especially retrieval-augmented generation (RAG) and model context protocols - choosing between RAG and Model Context Protocol (MCP) depends on the type, scope, and frequency of information being used.

Type of Information

• RAG is best for unstructured, text-heavy, and external data, such as articles, internal documentation, or web-based knowledge. The system retrieves relevant documents at query time and uses them to ground the model’s response. Example: A customer support chatbot answering questions from a constantly updated product knowledge base.
• MCP, by contrast, is better suited for structured, static, or short contextual information, such as predefined rules, configurations, or few-shot examples. This information is injected directly into the model’s prompt or system message. Example: A legal document summarizer configured with a fixed instruction to always use formal tone and extract clauses under specific headings.

Scope of Information

• RAG handles large or variable scopes of data that can't fit in the model's token limit. This allows querying millions of documents or gigabytes of text without overloading the context window. Example: A research assistant tool that fetches academic papers from a vector database like Pinecone or Weaviate and generates a literature review.
• MCP is suitable for narrow scope information that must be always available or explicitly controlled. Example: Injecting a product’s list of feature flags into the prompt so the AI can generate code or UI text tailored to those features.

Frequency of Updates

• RAG supports frequently updated or real-time information. Because it retrieves data at query time, any changes in the underlying knowledge base are immediately reflected in the output. Example: A financial assistant using real-time market data APIs or updated SEC filings to inform investment suggestions.
• MCP is appropriate for infrequently changing or session-persistent data that can be stored in memory, cache, or prompt templates. Example: Personalizing an AI shopping assistant with a user's saved preferences like shoe size, color choice, and budget.

RAG vs. MCP

Another point to consider is model context window. The model context window refers to the amount of text (tokens) that a language model can "see" or consider at one time when generating responses. This includes:

• Your input (prompt/question)
• The model's output (its response)
• Any previous turns in the conversation (in a chat)

Tokens: Finer details of tokens are covered in my previous article Monumental rise in AI reasoning: o1 to o4-mini.

• Tokens are chunks of text (words, parts of words, punctuation, etc.). For example, "ChatGPT is great!" is 5 tokens.
• The size of the context window determines how much information the model can use to generate a relevant response.
• If the conversation exceeds the context window, the oldest parts are truncated or dropped.

Token Efficiency Comparison: RAG vs. MCP

RAG Application Design        

A Retrieval-Augmented Generation (RAG) application enhances the capabilities of a language model by combining it with a document retrieval system. Instead of relying solely on the model's pre-trained knowledge, RAG dynamically fetches relevant documents from an external knowledge base (like a vector database) based on a user's query. These retrieved text snippets are then inserted into the model's prompt, providing grounded, up-to-date, and context-specific information to guide the generation of accurate and relevant responses. This approach is especially powerful in domains where real-time accuracy and domain knowledge are critical, such as customer support, legal, healthcare, and research applications.

Model Context Protocol (MCP)        

The Model Context Protocol (MCP) is a protocol developed by Anthropic that enables structured and modular interaction with AI models - particularly their context windows. It is designed to support tool use, memory, and external knowledge injection in a standardized and scalable way.

MCP allows developers to provide a model (like Claude) with multiple typed context blocks, such as:

• User inputs
• Tool outputs
• External documents
• Memory or planning state

Instead of sending all this as one long unstructured prompt, MCP lets you organize them into semantic sections, which the model can understand and use more effectively.

Key Features

• Typed Context Blocks: Each piece of context (e.g., a tool output, user query, or document) is given a label and type.
• Composable: You can mix and match different modules like memory, RAG results, and code outputs.
• Scalable: Useful for managing long-context applications without overwhelming the model.
• Model-Aware Parsing: Claude is designed to treat different blocks appropriately based on their types.

When To Use RAG or MCP        

When to Use RAG (Retrieval-Augmented Generation)

Use RAG when your AI agent needs to retrieve external or dynamic information at runtime:

Ideal for:

• Large, changing datasets (e.g., knowledge bases, product catalogs, web content).
• Fresh, time-sensitive content (e.g., news, stock prices, legal updates).
• Searching internal or external data sources (e.g., databases, vector stores).
• Retrieving relevant documents or facts.
• Search or document-intensive tasks (e.g., legal discovery, technical manuals, enterprise wikis).
• Personalized or organization-specific content stored externally (e.g., private Notion docs, Confluence pages).

Example Use Cases:

• “What is the return policy for this retailer?” (Pulled from website or help center)
• “Summarize the latest news on this topic.”
• “What did the customer say in their last three support tickets?”

When to Use Model Context Protocol (or Tool-Use Protocols)

Use MCP when you want to inject specific, structured, or fixed data directly into the model’s context (via prompt or system message):

Ideal for:

• Stable reference data (e.g., company values, tone of voice, documentation rules).
• Session-based personalization (e.g., user preferences or chat history).
• Function calling / structured tool use, like APIs or plugins.
• One-shot or few-shot learning with examples.

Example Use Cases:

• Injecting app-specific rules: "Always speak in a formal tone."
• Teaching model a proprietary format: “Convert this to our YAML schema.”
• Passing short-term memory into a session: user name, profile, location, shopping cart. conversation history, goals, preferences, task-specific parameters

Agentic AI App Interaction Flow With MCP Servers        

1. User query is received by the LLM interface.

• The LLM receives the prompt, possibly with a system prompt and some prior context.

2. The LLM evaluates whether a tool is needed.

• Using the model’s internal reasoning and prompt context, it determines if it should call a tool (e.g., for real-time data like weather, finance, etc.). The LLM is initialized with tool metadata in context (tool names, descriptions, schemas). There's no separate agent doing a check; it's part of the model's capabilities.

3. If a tool is needed, the LLM emits a tool call (function call / API call).

• It doesn't decide this through a separate "MCP Client"; rather, this is part of the model's learned behavior to issue tool calls via a structured output (like a JSON call with tool name and parameters).

4. The MCP / orchestrator (middleware) receives the tool call and routes it.

• This part does involve a server or middleware layer - what we're calling the "MCP Server" - that knows how to dispatch the request to the proper tool/plugin/API.

5. The selected tool/plugin executes the call using the passed parameters.

• The MCP server interacts with the weather plugin/tool and sends the request to the external Weather API.

6. The tool returns the result to the orchestrator, which passes it back to the LLM.

7. The LLM takes the tool's output and generates a natural-language response.

Combined Use Case (RAG + MCP)        

In complex applications, we can combine both:

• MCP: Sets rules, constraints, or structured expectations (e.g., db data, tone, format).
• RAG: Fetches relevant content on demand (e.g., documents, knowledge).

A practical agentic application flow :