As artificial intelligence (AI) systems grow increasingly sophisticated, their ability to seamlessly integrate with the real world becomes critical. The Model Context Protocol (MCP) is an open standard designed to bridge this gap, enabling lightweight yet powerful connections between AI models and the world around them converting them into agents as opposed to simple assistants.

In this session, we will cover:
- A high-level overview of MCP and its role in enhancing AI performance.
- Practical guidance on implementing MCP servers with real world examples in various programming languages, including JavaScript, Go, and Java.
- Common challenges faced during implementation such as integration complexity, distribution, and security considerations and best practices to overcome them.
- Key benefits of MCP, including its ability to make smaller models more effective by efficiently managing context and accessing relevant data.
- A demo showcasing the previous points.

By the end of the talk, you’ll have a clear understanding of how MCP servers work and how they simplify the development of autonomous AI agents.
You should also be able to start implementing your own tailored servers, empowering you to seamlessly integrate context-aware capabilities into your AI workflows.