Model Context Protocol

Model Context Protocol

Edge-first Raspberry Pi-based AI chatbot with privacy-preserving memory and context handling.

Bulletproof RAG agent with Context7 MCP integration, precise grounding, and a polished terminal UI, built for scalable developer workflows and reliable documentation retrieval.

Multi-agent document QA with context-aware retrieval, OpenAI-backed answers, and an interactive Streamlit UI.

Modular retrieval-augmented chatbot leveraging MCP to ingest multiple document types, perform semantic search, and generate responses with Gemini.

Lightweight Python implementation enabling seamless model-context sharing, dynamic tool integration, and extensible plugin support for AI workflows.

An image generator leveraging ModelScope FLUX to produce web-ready visuals with flexible prompts and multi-type outputs.

Generate AEM components, templates, dialogs using LLM prompts via Model Context Protocol

Lightweight, real-time streaming interface with voice cloning capabilities, accessible via a simple JSON manifest for easy client integration.

MCP-powered analysis of SOTA univariate anomaly models for constant target trends, offering automated literature benchmarking and docs.

Code QA MCP server emphasizing AST-based chunking, RAG with ChromaDB, and automated repo analysis to deliver precise, code-aware answers.

An MCP-powered AI agent that scrapes company news, analyzes sentiment and topics with GPT-4o, and renders dynamic charts via AntV.

Demonstrates sentiment analysis with a multi-tool agent using langgraph, featuring fast protocol communication and diverse transport options.

A Copilot integration that delivers context-aware, project-specific code suggestions through a modular prompt system and lightweight adapter.

A scalable integration of the Model Context Protocol enabling efficient propagation of rich context across distributed learning tasks.

Exercise: Integrate Model Context Protocol with GitHub Copilot

AI-driven vulnerability data collection and patch generation with end-to-end automation and deployment focus.

Exercise: Integrate Model Context Protocol with GitHub Copilot

Open protocol adds ontology-based semantics to service interactions, enabling capability-driven discovery, data grounding, and cross-domain tool integration for smarter LLM agents.

Integrates Copilot with a modular context protocol to enrich AI-assisted coding.

Agentic RAG MCP orchestrates specialized agents for ingestion, retrieval, and Gemini-powered answers via Streamlit.

Streamlit-based RAG chatbot grounding answers from PDFs and other formats using Google's Gemini API for verifiable, source-backed responses.

Agentic RAG Chatbot ingests multiple formats with modular agents, semantic search, and Gemini 2.5 for accurate document QA.