Presented by:Sam Basu
It is the age of AI - there is a huge boost to developer productivity and an opportunity to infuse apps with solutions powered by generative AI. The challenges with modern AI, however, are providing context to AI models and taking actions on deeply contextual tasks across disparate systems.
Model Context Protocol (MCP) can help - an open protocol to enable integration between LLM applications and external tools/data sources β essentially a way to provide more context to AI models. Developers can think of MCP as a common standardized language for information exchange between AI Agents/systems - MCP is growing in popularity and has seen rapid adoption in the AI community. Beyond the hype, letβs understand the promise of MCP and explore tooling to easily create MCP Servers/Clients. With official SDKs, it is a breeze to work with MCP and boost AI Model responses or surface tools to extend AI Agents. Developers could bring their own data, APIs, services and more through MCP - and have it surfaced through Agents in GitHub Copilot/Claude Code/Cursor. MCP provides a standardized protocol for bringing contextual expertise to the AI world and light up unique workflows for integrations β upwards and onwards.