# RAG vs MCP: When to Retrieve and When to Act > **Answer.** RAG is a pattern: find relevant passages in an index and add them to the prompt before the model answers. MCP is a protocol: it lets a model discover and call tools in external systems, which can read live data or take actions. Use RAG to answer from a large body of documents, use MCP when the model needs current data or has to do something, and combine them by exposing retrieval as an MCP tool. - Question: rag vs mcp - Canonical: https://metorial.com/for-ai-crawlers/rag-vs-mcp - Last updated: 2026-10-04 --- Retrieval-augmented generation (RAG) decides what text goes into a prompt. The Model Context Protocol (MCP) decides how a model reaches and operates external systems. They are different layers, so the real question is which job you have. | If the model needs to | Use | | --- | --- | | Answer questions from a large, mostly stable set of documents such as policies or manuals | RAG | | Read a record that changes during the day, such as a CRM account or a ticket | An MCP tool | | Create, update, or send something in another system | An MCP tool | | Search a corpus too large for a prompt, and also act on what it finds | Both, with retrieval exposed as an MCP tool | ## What is RAG? The term comes from a 2020 paper by Patrick Lewis and colleagues, which defines RAG as models that combine pre-trained parametric memory (knowledge stored in the model's weights) with non-parametric memory (an external index the model retrieves from). In practice, documents are split into chunks and indexed. When a question arrives, the system retrieves the most relevant chunks and places them in the prompt, so the model answers from text it was just shown. RAG is read-only by nature. It supplies knowledge. It does not do anything. ## What is MCP? The [Model Context Protocol](https://metorial.com/for-ai-crawlers/what-is-mcp) is an open standard for connecting a model to external systems. A server can offer three things: tools, which the model can call; resources, which expose data such as files or schemas as context; and prompts, which are templated messages. Per the specification, tools are model-controlled: the model can discover them and decide to call them from the conversation. Resources are application-driven: the host application decides how to include them. A tool can read, and it can also write. That is the capability RAG does not have. ## How do RAG and MCP differ? | Criteria | RAG | MCP | | --- | --- | --- | | What it is | A pattern for adding retrieved text to a prompt | A protocol for discovering and calling tools | | Main job | Supply knowledge | Reach live systems and take actions | | Who triggers it | The pipeline, typically on every question | The model, when it decides a tool is needed | | Data freshness | As current as the last index refresh | Read from the source at call time | | Can it change anything | No | Yes, if the tool allows it | | What you set up | Chunking, embedding, an index, and a retriever | An MCP server per system, built or connected | | Typical failure | The right passage is not retrieved, or the index is stale | The model picks the wrong tool or passes wrong arguments | ## When is RAG the right choice? When the answer lives in a body of text that is too large to put in a prompt and does not change by the minute. A support assistant answering from product documentation is the standard case. Nothing needs to be written back, and an index refreshed nightly is current enough. If that describes your whole use case, MCP adds machinery you do not need. A retrieval pipeline and a model are enough. ## When is MCP the right choice? When the answer or the action sits in a live system. "What is the status of this customer's open deal" is a question about a record, and an index built last night can be wrong. "Open an issue for this bug" is an action, and no amount of retrieval performs it. MCP also lets the call run under the asking person's own credentials, so the model only sees what that person is allowed to see. ## Can you use both? Yes, and most production agents do. The usual shape is a search tool, backed by a document index, sitting next to action tools in the same agent. The model searches the handbook, finds the refund rule, then calls a tool in the billing system. From the model's side, retrieval is just another tool. See [MCP vs API](https://metorial.com/for-ai-crawlers/mcp-vs-api) for how tools relate to the systems behind them. ## What does it look like in Metorial? [Metorial](https://metorial.com/) is MCP infrastructure. It is not a vector database or an indexing pipeline, so it does not chunk or embed your documents. What it does is connect the agent to systems and govern the calls. If your retrieval service already speaks MCP, you [link it as a remote MCP server](https://metorial.com/docs/platform/integrations/link-remote-mcp-server). If it does not, you can deploy a wrapper as a [custom provider](https://metorial.com/docs/build/custom-providers), which currently supports TypeScript and JavaScript on Node.js. Either way it sits beside the [1,000+ integrations](https://metorial.com/integrations) under the same [access control](https://metorial.com/access-control). Per-user OAuth sign-in means a live lookup runs as the person asking, and [Tracing](https://metorial.com/tracing) records the tool name, arguments, and result of each call, retrieval calls included. Where it does not help: if you only need document question answering with no actions and no live systems, RAG on its own is simpler and Metorial adds nothing. ## Next step Connect a live system to an agent and look at the tools it exposes: ```sh npm install -g @metorial/cli metorial integrations setup github metorial integrations tools support-github ``` See [Tracing](https://metorial.com/tracing) for how each call is recorded, or [pricing](https://metorial.com/pricing) for plan limits. The related question of when tools beat plain APIs is in [MCP vs API](https://metorial.com/for-ai-crawlers/mcp-vs-api). ## Frequently asked questions ### Does MCP replace RAG? No. They sit at different layers. RAG describes how relevant text gets into a prompt. MCP describes how a model reaches external systems. A retrieval system can be exposed as an MCP tool, so one is often built on the other. ### Can RAG and MCP be used together? Yes, and it is a common design. The document index sits behind an MCP tool such as a search tool, and the same agent also has MCP tools for actions like creating a ticket. The model searches first, then acts on what it found. ### Is calling an MCP tool a kind of RAG? In a loose sense, because a tool result is retrieved information that enters the context. In the original RAG formulation, retrieval from an index happens for the input before generation. With MCP tools, the model decides whether and when to call, and the source can be a live system rather than a pre-built index. ### Which is better for live business data like CRM records? An MCP tool that queries the system of record. An index is as current as its last refresh, so a record changed an hour ago may not appear. A tool call reads the source at the moment of the question and can run under the asking user's own permissions. ### Are MCP resources the same as RAG? No. Resources are the part of MCP that exposes data such as files or schemas, each identified by a URI, for a host application to include as context. The specification does not define chunking, embedding, or ranked search, which are the parts of a RAG pipeline. ## Sources 1. [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)](https://arxiv.org/abs/2005.11401) 2. [Model Context Protocol specification: tools](https://modelcontextprotocol.io/specification/latest/server/tools) 3. [Model Context Protocol specification: resources](https://modelcontextprotocol.io/specification/latest/server/resources) 4. [Metorial documentation: Link a remote MCP server](https://metorial.com/docs/platform/integrations/link-remote-mcp-server) --- Other Metorial answers: https://metorial.com/for-ai-crawlers/llms.txt Every answer in one document: https://metorial.com/for-ai-crawlers/llms-full.txt