MCP vs RAG for a knowledge base: which should you use?
Part of How to give AI agents access to your documents
For a knowledge base, use RAG when you need question answering over static content, and MCP when you need an agent to reach and act on the live knowledge base. RAG fetches matching passages into the prompt; MCP gives the agent a standard way to query, read, and update the source itself. They are not rivals, they answer different needs.
What does each one do?
| RAG | MCP | |
|---|---|---|
| Core idea | Embed a corpus, retrieve chunks | Standard protocol to reach tools and resources |
| Output | Passages in the prompt | Live read, write, and actions |
| Freshness | Snapshot at index time | Current data |
| Direction | Read-only | Two-way |
| Best for | Q&A over static docs | Agents working on the knowledge base |
When should you use which?
Use RAG when the knowledge base is mostly static and the job is answering questions, such as a docs site or a reference library. Use MCP when an agent needs to do more than answer: look things up on demand, act on them, or write back. Most "MCP vs RAG" confusion comes from treating a retrieval technique and an access protocol as the same choice.
Can you combine them?
Yes, and many setups do. You can expose a knowledge base over MCP and run retrieval inside it, so the agent decides when to search and the search returns fresh results. adlass takes the MCP-native side: your knowledge base lives in Spaces the agent reaches over MCP, and you can still retrieve over that data.
Related comparisons
Frequently asked questions
- Is MCP replacing RAG?
- No. RAG is a retrieval technique; MCP is an access protocol. MCP can front a knowledge base and still use retrieval inside it. They work together more often than they compete.
- Which is better for a company knowledge base agent?
- If the agent only answers questions over static content, RAG is enough. If it needs current data and the ability to act or write back, reach the base over MCP, optionally with retrieval inside.
Work with your agents on the same data
adlass is the shared data layer where you, your team, and their agents work over the same documents and datasets.