Model Context Protocol Gains Traction for AI Agents

- The Model Context Protocol (MCP) is an open standard developed by Anthropic, first introduced in November 2024, to create a universal way for AI models to connect with external data sources and tools. It is often described as the "USB-C port for AI" because it aims to standardize connections, similar to how USB-C provides a universal connector for various devices. - MCP operates on a client-server architecture. "MCP Hosts" are the AI applications, like an IDE or chat interface, that need to access external tools. These hosts contain "MCP Clients" which connect to "MCP Servers," the applications that expose tools and data for the AI to use. - Miro's MCP server was built in collaboration with several major tech companies, including Anthropic, AWS, GitHub, Google, and Windsurf (a Cognition company). This collaboration allows for bidirectional integration, meaning AI agents can both read information from Miro boards and write new content to them, such as generating diagrams from code. - The list of 11 AI coding platforms that Miro's MCP server connects with includes prominent tools such as Claude Code, AWS Kiro, GitHub Copilot, Gemini CLI, OpenAI Codex, and Devin. - A key problem MCP solves is the "N×M" data integration issue, where developers previously had to build custom connectors for every individual AI model and data source pairing. This protocol allows for a "write once, use everywhere" approach for developers creating tools for AI agents. - Early adopters of MCP after its late 2024 launch included developer-focused platforms like Codeium and Sourcegraph's Cody, which used it to connect AI assistants to live code repositories and developer workflows. Major AI labs, including OpenAI and Google DeepMind, also adopted the protocol by early 2025. - The protocol is designed to address the limitations of traditional APIs for AI agents by supporting stateful, two-way communication, which is more suitable for interactive AI applications than the granular, stateless nature of many REST APIs. It allows an AI to dynamically discover and use tools without needing them to be hard-coded. - Practical use cases for MCP extend beyond coding to include tasks like querying databases, managing files in systems like Google Drive, creating tickets in Jira, and even sending messages through Slack, all within a single conversational flow.