Loading…
Model Context Protocol (MCP) Bootcamp offers a deep dive into MCP architecture and its role in the Agentic AI ecosystem. Learn to build real-world, production-ready AI workflows using MCP with LangChain, LangGraph, and CrewAI through fully practical, project-based implementations.
Build-Agentic-AI-and-Gen-AI-Agents-with-MCP is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 52/100. Model Context Protocol (MCP) Bootcamp offers a deep dive into MCP architecture and its role in the Agentic AI ecosystem. Learn to build real-world, production-r. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Build-Agentic-AI-and-Gen-AI-Agents-with-MCP to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/Ratnesh-181998/Build-Agentic-AI-and-Gen-AI-Agents-with-MCP.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "Ratnesh-181998/Build-Agentic-AI-and-Gen-AI-Agents-with-MCP"
}Model Context Protocol (MCP) Bootcamp offers a deep dive into MCP architecture and its role in the Agentic AI ecosystem. Learn to build real-world, production-ready AI workflows using MCP with LangChain, LangGraph, and CrewAI through fully practical, project-based implementations.