Do not compare a retrieval architecture to a wire protocol as substitutes
The original RAG work combines a generator's parametric memory with retrieved non-parametric memory. MCP defines participants, discovery, primitives, and transports between an AI host and capability servers. Replacing a vector database with an MCP connection does not create retrieval; adding RAG does not create a reusable cross-client tool interface.
Choose RAG for a corpus question
Use RAG when the core task is finding relevant passages across a document collection, controlling chunking and ranking, preserving source metadata, and updating an index on a known schedule. Evaluate retrieval recall and answer grounding with a disclosed corpus and question set. Do not infer quality from the label 'RAG' alone.
Choose MCP for a capability boundary
Use MCP when multiple compatible hosts need to discover a server's tools, resources, or prompts; when the system must call live APIs or perform actions; or when you want a consistent protocol boundary around an existing service. MCP can expose data, but the server still owns authorization, query semantics, caching, and downstream reliability.
Use both for governed live knowledge workflows
A retrieval service can expose `search_documents` as a tool, a document or schema as a resource, and a guided research prompt. Another MCP server can then create an issue or update a system based on the retrieved evidence. Separate read-only retrieval credentials from action credentials and keep user confirmation between evidence and consequential writes.
Measure the same task before making performance claims
For a fair comparison, freeze a corpus, model, queries, top-k policy, MCP client/server versions, and downstream service. Capture retrieval metrics, end-to-end latency, context size, answer citations, and failures. This page does not generalize third-party latency or cost figures because those values depend on the exact implementation.