It is the age of AI, and there is a huge opportunity for developers to infuse apps with solutions powered by generative AI and large/small language models. Modern AI is big opportunity to streamline and automate developer workflows for better productivity. There are some challenges though – AI Models often lack context, and AI Agents need expertise to reliably pull off complex workflows across disparate systems.

Model Context Protocol (MCP) can help. Developed as an open standard, MCP aims to provide a standardized way to connect AI models to different data sources, tools and non-public information. The point is to provide deep contextual information/APIs/data as tools to AI models/agents—MCP services also support robust authentication/authorization toward executing specific tasks on behalf of users.

Developers can think of MCP as a common standardized language for information exchange between AI Agents/systems - MCP is growing in popularity and is showing a lot of promise as the emerging standard that bridges AI models with the tools they rely on. Beyond the hype, let’s 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 – upwards and onwards.