(watch full) Building MCP Agents from Scratch in Python | GAME CHANGER

🚀 Build a Next-Gen AI Agent from Scratch! | MCP + Gemini + Python + File Search Automation 🔍🤖 🎬 In this mind-blowing tutorial, we're not just creating any AI project — we're building a full-fledged Agentic AI Application using MCP architecture, LLM and Local Embedding Search! 💥 Learn how to: ✅ Build a file-search AI using natural language queries ✅ Create embeddings from local Linux files using Hugging Face models ✅ Integrate Gemini API (Google AI Studio) into your local apps ✅ Use MCP to control multiple agents with server-client architecture ✅ Apply cosine similarity, async Python, and more! 👨‍💻 Whether you're an AI developer, Python enthusiast, or a Linux power user, this project will blow your mind and level up your coding game! 🔑 Keywords for SEO: #aiapplications #agentAI #mcps #geminiapi #pythonai #linuxAI #ai #fileSearchAI #artificialintelligence #localLLM #langchain #googlegemini #opensourceai #semanticsearch #huggingface #embeddingSearch #reinforcementlearning #AsyncPython #MCPArchitecture #aiautomation #techtutorial #bionicalgorithm 👇 Grab the full code on GitHub & join the community: 🔗 [Add GitHub repo link here] 🔥 Don’t forget to LIKE, SHARE, and SUBSCRIBE to Bionic Algorithm for more cutting-edge AI content every week! 🧠 Stay curious. Build smart. Think Bionic. ⏱️ Timestamps / Chapters: 00:00 - 🚀 Introduction: What We’re Building Today 00:14 - 👋 Welcome to Bionic Algorithm Channel 00:25 - 📁 Problem Statement: Searching Files on Your System 00:45 - 🧠 Solution: Build an Agent AI for File Search 01:01 - ⚙️ Architecture Overview: LLM, MCP, and Agent Interaction 01:50 - 🧩 Understanding the Agent Design 02:26 - 🖥️ Linux Commands to List Files 03:05 - 📄 Creating Knowledge Base from File List 03:33 - 🔍 Generating Embeddings for Semantic Search 04:45 - 🤖 How Embedding Search Works (Cosine Similarity) 05:41 - 🛠️ Setting Up Python Environment (Conda + Python 3.11) 06:10 - 🔑 Setting Gemini API Key for LLM 07:17 - 🧠 Creating Gemini Client & Sending Prompts 08:11 - 🧾 File Indexing with Torch and Embedding Models 09:35 - 📊 Loading and Saving Embedding Files 10:10 - 🔄 Running Semantic Search with Query Embedding 11:00 - 🧠 MCP Server: Architecture and Functionality 12:03 - ⚙️ MCP Agent Logic Explained 13:08 - 🖥️ Running the MCP Server (Debugging and Testing) 14:00 - 🧩 Main Function to Handle User Query 15:05 - 🗂️ Query Processing and Prompt Engineering 16:30 - 🧬 Understanding the LM-MCP-Agent Communication 17:40 - 🛠️ Calling the Tool and Processing the Result 18:30 - 📝 Example 1: Searching for .env File 19:00 - 📝 Example 2: Searching for demo.txt 20:00 - 🧹 Cleanup and Indexing Best Practices 20:25 - ⚡ Enhancements: Faiss, Chromadb for Vector Search 20:45 - 🖥️ Platform Compatibility (Mac, Linux, Windows) 21:00 - 🎯 Conclusion: What We Built + Final Thoughts 21:15 - 💬 Like, Share, Comment & Subscribe! Links: Gemini API: https://aistudio.google.com/prompts/new_chat Embedding model: https://huggingface.co/ Code/Github: https://github.com/Code-Trees/Agentic_search LinkedIn : https://in.linkedin.com/in/pruthiraj-jayasingh