MCP, Cursor AI, Spring Boot and Java Tutorial
Large Language Models like GPT, Claude, and Gemini have transformed how we write, code, and automate tasks. However, despite their impressive capabilities, LLMs have two fundamental limitations:
1. Time-Locked Knowledge
LLMs are trained on large datasets, but those datasets have a cutoff point. This means that:
They can’t access real-time data such as current stock prices, system health dashboards, or live customer feedback.
Their responses are frozen in time, based on the most recent data available when their training ended.
For fast-changing domains—like cybersecurity, legislation, or internal corporate strategy—this makes LLMs outdated the moment they’re deployed.
2. No Access to Personal or Corporate Data
LLMs are not integrated into your local environment or your private infrastructure, so they cannot:
See your emails, project folders, CRM entries, or engineering logs.
Personalize answers based on your company’s tools, workflows, or policies.
Understand your unique naming conventions, team structure, or project priorities.
This makes it impossible for LLMs to provide truly contextualized or organization-specific answers out of the box.
How Model Context Protocol (MCP) Fixes These Gaps
The Model Context Protocol (MCP) is a lightweight interface protocol that enables LLMs to connect to real-time data sources and custom context providers in a structured, safe, and modular way. Here’s how it addresses the issues above:
✅ Live Context Injection
With MCP, developers can feed up-to-date, runtime information to the LLM just before the model generates a response. This allows the LLM to:
Access live business metrics, logs, or incident reports.
Pull in fresh data from APIs or databases to make decisions or explain results.
Always respond with contextual accuracy, even for rapidly changing environments.
✅ Personalized, Secure Data Access
MCP allows LLMs to receive structured data from personal or corporate context providers running locally or behind the firewall. This means:
LLMs can generate answers based on your company’s Jira tickets, Slack threads, or codebase.
Privacy is maintained because the data stays local and controlled—the model never gains unrestricted access.
The AI becomes tailored to your environment, giving more useful and accurate responses.
LLMs alone are powerful, but blind. MCP gives them sight—providing the real-time, personalized lens they need to operate in the modern, fast-moving enterprise environment. By making LLMs dynamic and context-aware, MCP transforms static generalists into adaptive, domain-specific assistants.
0:00 Introduction to MCP
1:35 Problems with LLMs
5:18 Spring AI and MCP
7:33 Spring MCP Starter
10:20 application.properties
12:18 Hibernate Book
13:40 Sevice Class
17:23 Spring Tool Annotation
20:31 ToolCallbackProvider
23:08 Bean Annotation!
24:01 JAR Build
25:10 Cursor MCP Config
27:02 MCP Server Enabled Check
27:35 MCP Cursor AI Test
29:00 Next Steps with MCP & Cursor AI