RAG vs MCP: Key Differences and Combined Use in AI Systems

RAG and MCP are often discussed as competing approaches, but they solve fundamentally different problems in AI systems. RAG gives models knowledge by retrieving information from documents, while MCP gives them agency by connecting to live APIs and enabling real-time actions.

The most powerful AI applications don't choose between them—they orchestrate both technologies together. With Stainless, you can generate both a production-ready SDK for your RAG pipeline and a fully functional MCP server from the same OpenAPI spec, letting you build comprehensive AI systems that can both reason about your data and act on it.
Retrieval-Augmented Generation (RAG) grounds models in knowledge, while the Model Context Protocol (MCP) lets them take action. They solve different problems, but their real power is unlocked when used together to build sophisticated AI systems. With Stainless, you can ship both a high-quality SDK for your RAG pipeline and a production-ready MCP server from a single OpenAPI spec in minutes.

What is retrieval-augmented generation?

Retrieval-Augmented Generation, or RAG, is a technique that enhances large language models by grounding them in external, up-to-date knowledge. It works by retrieving relevant information from a data source, like a vector database, and providing it to the model as context before generating a response. This helps reduce hallucinations and allows models to answer questions about proprietary or recent data they weren't trained on.

The process is a straightforward flow that forms the backbone of most modern AI-powered search and question-answering systems.

Retrieve and augment context

First, documents are broken down into smaller, manageable chunks. Each chunk is then converted into a numerical representation, called an embedding, and stored in a vector database for efficient searching.

When a user asks a question, the system converts the query into an embedding and uses it to find the most similar, and therefore most relevant, document chunks from the database. These retrieved chunks are combined with the original query and sent to the LLM as a single, context-rich prompt.

Diagnose rag limitations

While powerful, RAG has inherent limitations because it's fundamentally a read-only pattern.

• Stale Data: The knowledge base is only as current as its last update. If your data changes frequently, the RAG system can easily provide outdated information unless you constantly re-index it.

• Passive Responses: RAG can only answer questions based on existing documents. It can't perform actions, interact with live systems, or change the state of an application.

• Limited Scope: It's designed for unstructured data like text documents. It struggles to interact with structured, real-time data from APIs.

What is the model context protocol?

Model Context Protocol (MCP) is an open, JSON-RPC-based standard that allows LLMs to interact with external applications and services, and MCP is eating the world as more developers adopt it for building AI-powered applications. Think of it as a universal adapter that lets a model use tools to get live data or perform actions.

Expose APIs as tools

At its core, an MCP server exposes an API's endpoints as a list of tools, and the transition from API to MCP requires thoughtful structuring of these tools. Each tool has a name, a description that tells the model what it does, and a schema defining the parameters it accepts. The LLM uses this information to decide which tool to use and how to call it based on the user's request.

For example, a POST /users endpoint could become a create_user tool. The model understands from the tool's schema that it needs a name and email to execute the action.

Enable real-time actions

This tool-based approach directly addresses RAG's shortcomings. MCP is designed for real-time, interactive workflows.

• Live Data: Tools can call APIs to fetch the most current information, eliminating the problem of stale data.

• Stateful Actions: MCP allows models to perform write operations, like creating, updating, or deleting data.

• Secure Interactions: The protocol supports authentication flows like OAuth, ensuring that actions are performed securely on behalf of an authenticated user.

How RAG and MCP solve different problems

RAG and MCP are not competitors; they are complementary technologies designed for different jobs. RAG gives a model knowledge, while MCP gives it agency.

How to architect systems with rag and mcp

You don't have to choose between RAG and MCP. The most powerful AI systems use both, orchestrating them in patterns that leverage the strengths of each.

Here are three common architectural patterns for combining them.

1. Combine retrieval and action paths

In this pattern, the LLM is presented with two types of tools: a RAG tool for retrieving information from a knowledge base, and a set of MCP tools for performing actions. The model analyzes the user's request and decides which path to take.

For a query like "What were our sales last quarter?", the model would use the RAG tool to search financial reports. For a command like "Create an invoice for Acme Corp", it would use an MCP tool to call the invoicing API.

2. Chain rag before tool calls

Here, RAG acts as a preliminary step to inform an MCP tool call. The system first retrieves relevant documents to gather context, and then uses that context to help the LLM select the right MCP tool or populate its parameters.

A user might ask, "Summarize the latest support ticket from John Doe and draft a response." The system would first use RAG to find the ticket, then pass its contents to an MCP tool that calls the send_email API.

3. Orchestrate tools with dynamic discovery

For very large APIs, providing every endpoint as a distinct tool can overwhelm an LLM's context window, a challenge we've addressed while converting complex OpenAPI specs to MCP servers. Advanced MCP servers can instead provide a few meta-tools, such as list_api_endpoints, get_api_endpoint_schema, and invoke_api_endpoint.

In this architecture, the LLM can use RAG to search API documentation to understand what's possible. It then uses the dynamic MCP tools to discover the exact endpoint it needs and execute it, all at runtime.

How to ship MCP servers alongside RAG with Stainless

If you have an OpenAPI spec for your API, you can use the Stainless SDK generator to create both a high-quality SDK to power your RAG ingestion pipeline and a production-ready MCP server.

Generate servers from an openapi spec

You can add an MCP server to any TypeScript SDK project by enabling it in your configuration. This generates a subpackage within your SDK that can be published and deployed independently.

targets: 
  typescript:
    options:
      mcp_server:
        enable_all_resources: true

This simple configuration exposes all your API endpoints as tools, allowing you to generate an MCP server from an OpenAPI spec and have it fully functional in minutes. You can then selectively disable or customize tools to fine-tune the model's behavior.

Deploy servers to Cloudflare or Docker

Locally run MCP servers are great for development, but real-world applications often require remote servers that can handle authentication for web-based clients. You can generate a Cloudflare Worker that implements the necessary OAuth flow to securely connect your users.

For even greater flexibility, you can also publish your MCP server as a Docker image, making it easy to deploy to any environment.

Integrate servers with existing RAG pipelines

Your generated SDK and MCP server can work together seamlessly. For instance, you can use the SDK within your RAG pipeline to programmatically fetch data from your API and keep your vector database up-to-date.

This ensures your RAG system always has the freshest information to provide to the LLM, which can then use the MCP server to act on that information.

When to use RAG, MCP, or both together

Choosing the right approach depends entirely on what you want your AI system to accomplish.

• Use RAG alone when you need a knowledge bot that can answer questions about a static set of documents. This is perfect for customer support chatbots or internal documentation search.

• Use MCP alone when you need an agent that performs specific, well-defined tasks against live data. Think of a workflow automator that can create a new user, assign a task, or check an order status—capabilities that demonstrate why an API isn't finished until the SDK ships.

• Use both together when you need a true AI assistant or copilot. This system can reason about your data, answer complex questions, and then take action based on its conclusions, offering the most powerful and flexible user experience.

Frequently asked questions about RAG and MCP

Is MCP better than RAG?

Neither is "better"; they are designed for different purposes. RAG is for retrieving knowledge from static documents, while MCP is for executing actions against live APIs.

Can I run RAG and MCP in one repo?

Yes, a common pattern is to manage both from a single source of truth. With a tool like Stainless, your OpenAPI spec can generate both the SDK for your RAG pipeline and your MCP server.

How do I secure MCP servers in production?

Production MCP servers should be secured using standard web practices. This includes implementing OAuth 2.0 for user authorization, using scoped access tokens, and deploying behind a firewall with rate limiting.

Should I start with RAG or MCP first?

Start with the technology that solves your most immediate problem. If users need to ask questions about your data, start with RAG. If they need to automate tasks, start with MCP.

How do clients handle schema differences?

Different LLM clients have varying support for complex JSON schemas. A robust MCP server can automatically transform its tool schemas to match the capabilities of the connected client, ensuring broad compatibility.

Ready to build? Get started for free.