MCP vs RAG: How They Work Together (2026)

RAG Workflow

┌─────────────────────────────────────────────┐
│  User Question: "What's the latest on      │
│  React 19 features?"                        │
└──────────────────┬──────────────────────────┘
                   │
                   ▼
         ┌─────────────────┐
         │  1. RETRIEVE    │
         │  Search web for │
         │  React 19 docs  │
         └────────┬────────┘
                  │
                  ▼
         ┌─────────────────┐
         │  2. AUGMENT     │
         │  Add docs to    │
         │  LLM context    │
         └────────┬────────┘
                  │
                  ▼
         ┌─────────────────┐
         │  3. GENERATE    │
         │  LLM answers    │
         │  with context   │
         └────────┬────────┘
                  │
                  ▼
         ┌─────────────────┐
         │  Response with  │
         │  current info   │
         └─────────────────┘

MCP Architecture

┌─────────────────────────────────────────────┐
│  MCP Client (Claude Desktop, Cursor, etc.)  │
└──────────────────┬──────────────────────────┘
                   │ MCP Protocol (JSON-RPC)
                   │
    ┌──────────────┼──────────────┐
    │              │              │
    ▼              ▼              ▼
┌────────┐    ┌────────┐    ┌────────┐
│GitHub  │    │Postgres│    │Slack   │
│Server  │    │Server  │    │Server  │
└────────┘    └────────┘    └────────┘
    │              │              │
    ▼              ▼              ▼
┌────────┐    ┌────────┐    ┌────────┐
│GitHub  │    │Database│    │Slack   │
│API     │    │        │    │API     │
└────────┘    └────────┘    └────────┘

Example: Research → Code Workflow

User: "Research Next.js 14 app router best practices,
       then create a GitHub issue with recommendations"

Step 1 (RAG): Exa MCP Server
  → Searches web for Next.js 14 app router content
  → Returns: Documentation, blog posts, examples

Step 2 (Reasoning): Claude
  → Analyzes retrieved content
  → Synthesizes best practices
  → Formulates issue description

Step 3 (Action): GitHub MCP Server
  → Creates GitHub issue with title + body
  → Returns: Issue URL

Result: Fully automated research-to-action pipeline

Complete RAG + MCP System

┌──────────────────────────────────────────────┐
│  User: "Find security vulnerabilities in     │
│   our codebase and create Jira tickets"      │
└─────────────────┬────────────────────────────┘
                  │
    ┌─────────────┴─────────────┐
    │                           │
    ▼                           ▼
┌─────────────┐          ┌─────────────┐
│ RAG LAYER   │          │ MCP TOOLS   │
│ (Knowledge) │          │ (Actions)   │
└─────────────┘          └─────────────┘
    │                           │
    ▼                           ▼
1. Exa MCP Server          4. GitHub MCP Server
   → Search CVE               → Read codebase
   databases
                           5. Jira MCP Server
2. Filesystem MCP             → Create tickets
   → Read code files
                           6. Slack MCP Server
3. Postgres MCP               → Notify team
   → Query past
   vulnerabilities

┌──────────────────────────────────────────────┐
│  Claude orchestrates all servers,            │
│  using RAG for context and MCP for actions   │
└──────────────────────────────────────────────┘

File: claude_desktop_config.json

{
  "mcpServers": {
    // RAG-focused servers (retrieval)
    "exa": {
      "command": "npx",
      "args": ["-y", "@exa/mcp-server"],
      "env": {
        "EXA_API_KEY": "your_exa_api_key"
      }
    },
    "brave-search": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "your_brave_api_key"
      }
    },

    // Action-focused servers (execution)
    "notion": {
      "command": "npx",
      "args": ["-y", "@notionhq/mcp-server"],
      "env": {
        "NOTION_TOKEN": "your_notion_integration_token"
      }
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "your_github_token"
      }
    }
  }
}

rag_mcp_server.py

from mcp.server import Server
from openai import OpenAI
import requests
import numpy as np

server = Server("rag-mcp-server")
client = OpenAI()

# In-memory vector store (use Pinecone/Weaviate in production)
knowledge_base = []

@server.tool()
async def ingest_documents(urls: list[str]) -> str:
    """Ingest documents from URLs into the RAG knowledge base

    Args:
        urls: List of URLs to scrape and embed
    """
    for url in urls:
        # Fetch content (simplified - use Firecrawl in production)
        response = requests.get(url)
        content = response.text[:5000]  # Limit size

        # Generate embedding
        embedding_response = client.embeddings.create(
            model="text-embedding-3-small",
            input=content
        )
        embedding = embedding_response.data[0].embedding

        # Store in knowledge base
        knowledge_base.append({
            "url": url,
            "content": content,
            "embedding": embedding
        })

    return f"Ingested {len(urls)} documents into knowledge base"


@server.tool()
async def semantic_search(query: str, top_k: int = 3) -> str:
    """Search knowledge base using semantic similarity (RAG retrieval)

    Args:
        query: Natural language search query
        top_k: Number of results to return (default 3)
    """
    if not knowledge_base:
        return "Knowledge base is empty. Use ingest_documents first."

    # Get query embedding
    query_response = client.embeddings.create(
        model="text-embedding-3-small",
        input=query
    )
    query_embedding = query_response.data[0].embedding

    # Compute similarities
    results = []
    for doc in knowledge_base:
        similarity = np.dot(query_embedding, doc["embedding"])
        results.append({
            "url": doc["url"],
            "content": doc["content"][:500],  # Truncate
            "similarity": similarity
        })

    # Sort and return top_k
    results.sort(key=lambda x: x["similarity"], reverse=True)
    top_results = results[:top_k]

    output = "\n\n---\n\n".join([
        f"[{r['similarity']:.3f}] {r['url']}\n{r['content']}"
        for r in top_results
    ])

    return output


@server.tool()
async def rag_answer(question: str) -> str:
    """Answer a question using RAG (retrieve + generate)

    Args:
        question: Question to answer using knowledge base
    """
    # Step 1: Retrieve relevant context
    context = await semantic_search(question, top_k=3)

    # Step 2: Generate answer with context
    chat_response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {
                "role": "system",
                "content": "Answer questions based only on the provided context."
            },
            {
                "role": "user",
                "content": f"Context:\n{context}\n\nQuestion: {question}"
            }
        ]
    )

    return chat_response.choices[0].message.content


if __name__ == "__main__":
    server.run()

claude_desktop_config.json

{
  "mcpServers": {
    "rag-server": {
      "command": "python",
      "args": ["rag_mcp_server.py"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key"
      }
    }
  }
}

User Query
    │
    ▼
Retrieve Context (RAG)
    │
    ▼
Generate Response (LLM)
    │
    ▼
Return Answer

Use when: Pure Q&A, no actions needed

User Command
    │
    ▼
Execute MCP Tools
    │
    ▼
Return Results

Use when: Clear actions, no research needed

User Request
    │
    ▼
1. RAG Retrieval
   (Exa, Brave, Filesystem)
    │
    ▼
2. LLM Reasoning
   (Synthesize findings)
    │
    ▼
3. MCP Action
   (GitHub, Slack, Notion)
    │
    ▼
Result + Confirmation

Use when: Complex agentic workflows