I made an MCP Agent That Finds Jobs Based on Your LinkedIn Profile

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Recently, I was exploring the OpenAI Agents SDK and building MCP agents and agentic Workflows. To implement my learnings, I thought, why not solve a real, common problem? So I built this multi-agent job search workflow that takes a LinkedIn profile as input and finds personalized job opportunities based on your experience, skills, and interests. I used: - OpenAI Agents SDK to orchestrate the multi-agent workflow - Bright Data MCP server for scraping LinkedIn profiles & YC jobs. - Nebius AI models for fast + cheap inference - Streamlit for UI (The project isn't that complex - I kept it simple, but it's 100% worth it to understand how multi-agent workflows work with MCP servers) Here's what it does: - Analyzes your LinkedIn profile (experience, skills, career trajectory) - Scrapes YC job board for current openings - Matches jobs based on your specific background - Returns ranked opportunities with direct apply links Here's a walkthrough of how I built it: https://www.youtube.com/watch?v=zNTWmw72BDs The Code is public too: https://github.com/Arindam200/awesome-ai-apps/tree/main/adva... Give it a try and let me know how the job matching works for your profile!

Visit product View on Hacker NewsMay 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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