Education & Research liveProSubmitted August 1, 2026

Code-To-Career: An Agentic Learning Platform with Job-Market Grounding & MCP-Powered Mentorship

An MCP app on the Model Context Protocol built by livePro at the SRMIST NitroStack × MCP To The Moon hackathon and deployed on NitroStack.

About this project

The Problem Students learning to code today face a fragmented, overwhelming ecosystem. They suffer from "Tutorial Hell" because they don't have clear roadmaps aligned with real job requirements. They lack access to senior mentors and realistic interview practice. Furthermore, they are disconnected from live tech news and developer communities. Even worse, just starting to code requires hours of downloading compilers, configuring environment variables, and setting up complex workspaces for every new language they want to learn. How We Solve It Code-To-Career is an all-in-one, AI-powered career accelerator that solves this entire lifecycle in a single platform: Zero-Setup AI Code Editor: A built-in, AI-powered code reviewer and editor removes environment setup friction entirely. Students can write, review, and debug code immediately in the browser. Market-Grounded Roadmaps: Instead of generic advice, our autonomous agent searches the live LinkedIn Jobs API for the student's desired skill, extracts real employer requirements, and feeds that to Gemini. The resulting roadmap is grounded in what the industry actually demands. Context-Aware Mentorship via MCP: Access to mentorship is democratized via our custom Model Context Protocol (MCP) server. It securely exposes the student’s live database state (current roadmaps, milestones, and weak areas) to the AI via a resource:// protocol. The AI mentor literally "reads" their progress before replying, enabling hyper-personalized guidance. Adaptive Interview Prep: An AI mock-interview agent dynamically generates adaptive MCQs based strictly on the skills in the user's active roadmaps, providing instant feedback. Community & News: An integrated Q&A community and live-updating Tech News feed keep students connected to the industry. What Makes it Special? By combining Agentic workflows (taking action before generating content) with novel MCP primitives (exposing database state as context resources), we’ve built an ecosystem that

Education & Research track

Build innovative tools that transform learning, teaching, academic research, and knowledge discovery.

Live MCP appSource code

Team livePro

  • Vishal Deep

  • BHAAVINI KADIRSAKTHILead

  • Maaz Farooqui

  • Komaravalli Monish

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Frequently asked questions

What does Code-To-Career: An Agentic Learning Platform with Job-Market Grounding & MCP-Powered Mentorship do?
The Problem Students learning to code today face a fragmented, overwhelming ecosystem. They suffer from "Tutorial Hell" because they don't have clear roadmaps aligned with real job requirements. They lack access to senior mentors and realistic interview practice. Furthermore, they are disconnected from live tech news and developer communities. Even worse, just starting to code requires hours of downloading compilers, configuring environment variables, and setting up complex workspaces for every new language they want to learn. How We Solve It Code-To-Career is an all-in-one, AI-powered career accelerator that solves this entire lifecycle in a single platform: Zero-Setup AI Code Editor: A built-in, AI-powered code reviewer and editor removes environment setup friction entirely. Students can write, review, and debug code immediately in the browser. Market-Grounded Roadmaps: Instead of generic advice, our autonomous agent searches the live LinkedIn Jobs API for the student's desired skill, extracts real employer requirements, and feeds that to Gemini. The resulting roadmap is grounded in what the industry actually demands. Context-Aware Mentorship via MCP: Access to mentorship is democratized via our custom Model Context Protocol (MCP) server. It securely exposes the student’s live database state (current roadmaps, milestones, and weak areas) to the AI via a resource:// protocol. The AI mentor literally "reads" their progress before replying, enabling hyper-personalized guidance. Adaptive Interview Prep: An AI mock-interview agent dynamically generates adaptive MCQs based strictly on the skills in the user's active roadmaps, providing instant feedback. Community & News: An integrated Q&A community and live-updating Tech News feed keep students connected to the industry. What Makes it Special? By combining Agentic workflows (taking action before generating content) with novel MCP primitives (exposing database state as context resources), we’ve built an ecosystem that
Who built Code-To-Career: An Agentic Learning Platform with Job-Market Grounding & MCP-Powered Mentorship?
Code-To-Career: An Agentic Learning Platform with Job-Market Grounding & MCP-Powered Mentorship was built by team livePro at the SRMIST NitroStack × MCP To The Moon hackathon, in the Education & Research track.
What is an MCP app and how is it built?
An MCP app is an application built on the Model Context Protocol — an open standard that lets AI agents connect to tools, data, and APIs. This project exposes MCP tools and resources that agentic AI systems can call. It was built and deployed on NitroStack, the full-stack platform for shipping MCP apps and servers.