MCP vs RAG: how should AI agents get context?
Short answer: RAG is how you find relevant text; MCP is how an agent talks to a context source. They are not competitors; the strongest setups use both. Confusing them leads to agents that retrieve plausible snippets but still act on stale or contradictory information.
RAG finds similar passages and cannot tell you which source wins
RAG has no concept of authority, freshness across tools (the ticket changed after the doc was written), or contradiction (two sources disagree).
Retrieval-Augmented Generation embeds a corpus, finds passages similar to a query, and stuffs them into the prompt. For a single, mostly-static knowledge base, that is often all you need.
MCP standardizes how an agent asks for context and leaves the answer to you
Serving RAG results over MCP upgrades the interface and leaves the answer exactly as good as it was. The protocol defines the pipe; you still choose what flows through it. Read the specification and the gap is explicit: at revision 2025-11-25 the MCP Resource type declares nine fields and none of them records which source is authoritative.
The Model Context Protocol is an open interface an agent uses to ask for context and to write observations back. It standardizes the conversation between an agent and a context source, regardless of what is behind it.
The strongest setup runs retrieval inside a governed context layer and serves it over MCP
A governed context layer behind MCP is what turns a retrieved passage from an answer into a candidate.
| Concern | RAG alone | MCP + context layer |
|---|---|---|
| Find relevant text | ✅ | ✅ |
| Resolve duplicate identities | ❌ | ✅ |
| Authority per fact (which source wins) | ❌ | ✅ |
| Withhold contested facts | ❌ | ✅ |
| Agents write back | ❌ | ✅ |
The agent asks over MCP, retrieval surfaces candidates, and the governance layer decides what is trustworthy enough to serve. See how LoomSignal structures this.
Choosing MCP over RAG is choosing an interface over a search technique
Don't pick MCP or RAG. Use RAG as one signal, MCP as the interface, and a governed context layer to make the answer something an agent can safely act on.
Frequently asked questions
No. RAG is a retrieval technique; MCP is a protocol for how an agent talks to a context source. You can serve RAG results over MCP; they operate at different layers.
When you need to pull relevant passages from a static corpus and don't care about authority, freshness across tools, or contradictions. For a single knowledge base, RAG alone is fine.
MCP standardizes how an agent asks a source for context, and RAG returns the passages most similar to the query. Neither one decides which source is authoritative for a fact. A governed context layer supplies that decision: it sits behind the MCP interface with retrieval running inside it, reconciles the many names of one entity into one, and flags contested facts instead of blending them into one confident answer. The agent asks over MCP, retrieval surfaces candidates, the layer decides what is trustworthy enough to serve, and the agent writes observations back through the same MCP interface.