MCP & AI TOOLS
MCP vs RAG
RAG is a way of giving a model passages to read. MCP is a way of giving it tools to call. One is an approach to retrieval; the other is a plug.
- Documentspolicies · manuals
- Similar passages
- Promptthe model reads them
- Answers found by reading
- Cannot add up a column
- AI client
- MCP servertools with schemas
- Structured resultrows · counts
- Any tool, called with arguments
- A retrieval tool can be one of them
A technique and a protocol
The two get compared because both arrived with the same wave of AI tooling and both are about getting a model to work with information it was not trained on. Beyond that they are different kinds of thing. Retrieval-augmented generation is a technique for answering from documents: break them into passages, embed them, find the passages most similar to the question, and put those in the prompt so the model answers from them. The Model Context Protocol is a standard for connecting an AI client to tools: the client asks a server what it offers, calls a tool with structured arguments, and gets a structured result back. One describes how to retrieve text; the other describes how to plug anything in.
What RAG is for
Questions whose answers are written down somewhere. What the return policy allows, how the warranty claim works, which clause governs early termination. The answer exists as a passage, the question is a request to find and summarize it, and similarity search by meaning is good at finding passages even when the wording differs. RAG is also where it stops being useful: a passage is not a computation. "Total revenue by region" is not written down anywhere, and a model handed the ten passages most similar to that question will produce a fluent paragraph with a plausible number in it. Three ways AI answers from your data goes through that failure.
What MCP is for
Giving a model things it can call, in a form it can discover. A server publishes tools - search this collection, count by this field, fetch this record, look up this document - each with a description the model reads and a schema for its arguments. The client fetches the list when it connects, chooses a tool while answering, calls it, and reasons over what comes back. The protocol says nothing about what the tools do. A tool can run a facet over an index and return exact counts; another tool on the same server can run a passage search over a manual. What is MCP? explains the parts.
Side by side
| Criterion | RAG | MCP |
|---|---|---|
| What it is | A retrieval technique | A connection protocol |
| What moves | Passages of text into the prompt | Tool calls out, structured results back |
| Unit | A chunk of a document | A tool with a name, a description and a schema |
| Good at | Questions answered by reading: policies, manuals, contracts | Anything a tool can do: search, count, fetch, act |
| Numbers | Not computedA passage may contain a figure; nothing is added up | If a tool computes themA facet tool returns exact counts |
| Where it runs | A vector store beside the model | A server, local or remote, that the client connects to |
| Typical failure | The right passage was not retrieved | The wrong tool was chosen, or the right one described badly |
| Can they combine? | As a tool behind MCP | As the plug for RAG and for data |
Why they are not alternatives
Ask "which should we use, RAG or MCP?" and the honest answer is that the question has a category error in it. If the need is answers from documents, RAG is one way to build the retrieval, and an MCP server is one way to expose it to many clients. If the need is numbers from records, RAG is the wrong technique regardless of how it is connected, and a facet tool behind MCP is the right one. A company with both needs builds both tools and serves them from one place.
- AI clientone connection
- MCP servertwo tools
- search_documentsRAG over manuals
- facetcounts over orders
The combination is also where MCP earns its keep. A model connected to one server that offers search_documents and facet can answer "what does the policy say about rejected orders, and how many do we have this week?" by calling both, in one conversation, without anyone having built an integration for that pairing. The tools were described; the model composed them.
The practical question
For business data the useful comparison is not RAG against MCP but RAG against indexed retrieval, because that is the choice about where numbers come from. Indexed retrieval has the model choose collections, fields and facets from a constrained vocabulary and lets the index compute; it is exact, it never touches the production database, and it is the approach a facet tool implements. RAG is for the documents around the data. MCP is how either reaches the AI tools a team already uses. MCP server explained covers what such a server should and should not publish.
When each is right
RAG, when the answer is a passage. A support team asking what the warranty covers, a new hire asking how expenses are filed, a lawyer asking which clause applies. The source is prose, the answer is prose, and finding the right page by meaning is the hard part. Build the retrieval well - clean chunks, good embeddings, a citation back to the page - and the model does the rest.
Indexed tools behind MCP, when the answer is a number or a list. How many orders were rejected this week, which region leads, which products are low in stock. The source is records, the answer is computed, and no passage contains it. A facet tool returns the count; the model reports it and never has to arithmetic.
Both, when the question is mixed. "Which customers are affected by the policy change?" needs the policy text and a filtered list of records. One server with a document tool and a data tool answers it in one turn, and neither technique had to pretend to be the other.
What to ask a vendor
When a product says it "uses RAG" for business questions, ask how it adds up a column. When a product says it "supports MCP", ask what tools the server publishes and what each returns. The first question separates passages from numbers. The second separates a plug from the appliance behind it.