MCP vs RAG: A Practical Guide for Data Engineers and AI Builders - InfoCaptor AI

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RAG vs MCP

There is a growing discussion in the AI engineering community about two powerful but distinct paradigms: RAG (Retrieval-Augmented Generation) and MCP (Model Context Protocol). Both extend what language models can do, but they address very different challenges. Understanding their differences—and how they complement each other—is key to designing effective AI-enabled data systems.

What is RAG?

Retrieval-Augmented Generation (RAG) is a technique where a language model augments its knowledge by retrieving external information—usually from a vector database containing document embeddings.

  • How RAG works
    1. You embed your documents (PDFs, transcripts, articles, etc.) into vectors.
    2. Store those embeddings in a vector database.
    3. At query time, embed the user’s question and retrieve the most relevant chunks.
    4. Pass the retrieved text into the model prompt for grounding.
  • Strengths
    • Handles large collections of unstructured text.
    • Keeps LLM outputs grounded in your domain-specific documents.
    • Ideal for knowledge bases, research papers, support articles, or wikis.
  • Limitations
    • Data freshness depends on re-embedding documents.
    • Does not inherently understand schemas, databases, or live system states.

In short, RAG gives an LLM a memory of documents.

What is MCP?

Model Context Protocol (MCP) is a new standard for connecting LLMs to external tools and data in a structured, real-time way.

  • How MCP works
    1. You run an MCP server that exposes tools or resources (for example, sql.query, profile.table, searchTickets).
    2. An MCP-aware LLM client (such as Claude Desktop or a VS Code extension) discovers these tools automatically.
    3. When you ask a question, the LLM decides which tools to call.
    4. The client invokes the MCP server, receives structured JSON results, and feeds them back into the model’s context.
  • Strengths
    • Provides live access to structured systems (databases, APIs, logs, catalogs).
    • Always fresh, since it queries at runtime.
    • Safe and composable: you expose only the functions you choose.
  • Limitations
    • Not suited for semantic search over long unstructured text corpora.

In short, MCP gives an LLM eyes and hands to interact with your systems.

How RAG and MCP Work Together

The two approaches are not competitors—they are complementary.

  • RAG = memory (semantic recall of unstructured text).
  • MCP = eyes and hands (live access to structured systems).

For example:

  • RAG finds a troubleshooting document about billing complaints.
  • MCP queries your live SQL database to check how many users are currently missing billing information.

Together, they create a complete workflow: recall knowledge from documents and act on live structured data.

Frequently Asked Questions

Below are common questions engineers ask when first exploring MCP. Each answer builds on the concepts above and includes working code examples.

What is an MCP server? Is it just repeated LLM calls?

An MCP server is not simply a loop that makes repeated calls to a language model. Instead, it is a structured adapter layer that exposes safe, discoverable functions and resources to any MCP-aware client.

The model does not have to guess how to call an API or query a database—it sees a schema-defined tool like sql.query or profile.table. Communication happens over JSON-RPC, not ad hoc prompts.

How can MCP help with data gaps or data engineering?

MCP is especially powerful in data engineering and analytics contexts. Here are several concrete use cases:

  1. Expose real data sources
    • SQL databases (Snowflake, Databricks, Postgres).
    • Data catalogs (Unity Catalog, Alation, Collibra).
    • Object storage systems like S3.
  2. Detect data gaps
    • Use MCP tools to run profiling functions and surface missing values.
    • Trace lineage to see where gaps originate.
  3. Automate data quality checks
    • Tools such as check_freshness or validate_schema can be directly exposed.
    • The LLM can run these checks iteratively without manual exports.
  4. Fill data gaps
    • Connect enrichment APIs through MCP (e.g., geocoding, enrichment services).
    • The LLM can combine profiling with enrichment automatically.
  5. Cross-system correlation
    By running multiple MCP servers side by side (for databases, Jira tickets, GitHub repositories), the LLM can correlate pipeline failures with code changes and tickets.

How does the LLM interact with MCP?

The LLM does not run inside the MCP server. Instead:

  1. The MCP server defines available tools.
  2. An MCP-aware client (like Claude Desktop) launches the server and tells the LLM what tools exist.
  3. When you ask a question, the LLM decides which tool to call.
  4. The client sends a JSON-RPC request to the MCP server.
  5. The server executes the action and returns structured JSON.
  6. The client injects the JSON back into the LLM’s context, grounding the answer.

This separation makes the system safe and modular.

Is MCP an alternative to RAG?

No. MCP and RAG solve different problems.

  • RAG is best for unstructured documents and long text retrieval.
  • MCP is best for structured data, live queries, and APIs.
FeatureRAGMCP
Data typeUnstructured docsStructured data & APIs
FreshnessNeeds re-embeddingAlways live
Model roleReads chunksCalls functions
Best forKnowledge basesData pipelines, metadata, live systems

Think of it like this:

  • RAG = memory (semantic recall of documents).
  • MCP = eyes & hands (live access to systems).

In practice, they are complementary. A complete AI system often uses both.

Example 1: A Simple MCP Server for SQL Profiling

Below is a Node.js MCP server that exposes two safe, read-only tools:

  • sql.query — executes SELECT-only SQL
  • profile.table — runs a quick null-percentage profile on a table

package.json

{
  "name": "mcp-sql-toy",
  "private": true,
  "type": "module",
  "dependencies": {
    "@modelcontextprotocol/sdk": "^0.1.0",
    "better-sqlite3": "^9.4.0"
  },
  "scripts": {
    "start": "node server.js",
    "seed": "node seed.js"
  }
}

seed.js

import Database from 'better-sqlite3';

const db = new Database('demo.db');
db.exec(`
  DROP TABLE IF EXISTS customers;
  CREATE TABLE customers(
    id INTEGER PRIMARY KEY,
    name TEXT,
    email TEXT,
    city TEXT
  );

  INSERT INTO customers (name, email, city) VALUES
    ('Ada Lovelace', 'ada@example.com', 'London'),
    ('Alan Turing',  NULL,              'Manchester'),
    ('Grace Hopper', 'grace@example.com', NULL),
    ('Linus Torvalds', NULL,            'Helsinki');
`);
console.log('Seeded demo.db');

server.js

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import Database from "better-sqlite3";

const db = new Database("demo.db", { fileMustExist: true });

function assertSelectOnly(sql) {
  const s = sql.trim().toUpperCase();
  if (!s.startsWith("SELECT")) {
    throw new Error("Only SELECT queries are allowed.");
  }
}

function profileNulls(table) {
  const pragma = db.prepare(`PRAGMA table_info(${table})`).all();
  if (pragma.length === 0) throw new Error(`Table not found: ${table}`);
  const { cnt } = db.prepare(`SELECT COUNT(*) as cnt FROM ${table}`).get();
  const columns = {};
  for (const c of pragma.map(c => c.name)) {
    const { n } = db.prepare(`SELECT COUNT(*) as n FROM ${table} WHERE ${c} IS NULL`).get();
    columns[c] = { null_count: n, null_pct: cnt ? +((n / cnt) * 100).toFixed(2) : 0 };
  }
  return { table, row_count: cnt, columns };
}

const server = new Server(
  { name: "mcp-sql-toy", version: "0.1.0" },
  {
    capabilities: {
      tools: {
        "sql.query": {
          description: "Run a SELECT-only SQL query against demo.db",
          inputSchema: {
            type: "object",
            properties: { sql: { type: "string" } },
            required: ["sql"]
          },
          handler: async ({ sql }) => {
            assertSelectOnly(sql);
            const rows = db.prepare(sql).all();
            return { content: [{ type: "json", data: rows }] };
          }
        },
        "profile.table": {
          description: "Return null% profile for a table",
          inputSchema: {
            type: "object",
            properties: { table: { type: "string" } },
            required: ["table"]
          },
          handler: async ({ table }) => {
            const prof = profileNulls(table);
            return { content: [{ type: "json", data: prof }] };
          }
        }
      }
    }
  }
);

await server.connect(new StdioServerTransport());
console.error("MCP SQL server running on stdio");

Running this example:

npm install
npm run seed
npm start

An MCP-aware client (like Claude Desktop) can now query live data via this server.

Example 2: A Hybrid MCP Server with SQL and RAG

The next example combines structured and unstructured access:

  • sql.query — read-only SQL queries.
  • profile.table — null profiling.
  • rag.search — semantic search using TF-IDF over local documents.

[Full hybrid code as shown in the conversation can be included here for readers.]

This demonstrates how an MCP server can unify structured queries and unstructured semantic recall in one system.

Conclusion

  • RAG is a technique for grounding models in unstructured text. It excels at semantic document recall.
  • MCP is a protocol for exposing structured, live tools and data to LLMs. It excels at working with databases, APIs, and operational systems.
  • They are not alternatives but complements. A production-ready AI system often uses both: RAG for memory and MCP for live system access.

For data engineers, MCP offers a direct way to expose profiling, lineage, data quality checks, and APIs to language models—without unsafe guessing. For knowledge-heavy domains, RAG remains essential for document recall.

Together, they transform LLMs from passive text predictors into active copilots capable of both remembering and acting.