The Model Context Protocol (MCP) is an open standard that defines how LLM applications and agents integrate with external data sources and tools, significantly improving the quality of AI-generated outputs. MCP focuses on context and tool access, distinguishing it from the Agent2Agent (A2A) protocol, which governs inter-agent communication. It specifies servers (for data and tools such as databases, wikis and services) and clients (agents, applications and coding assistants). Since our last blip, MCP adoption has surged, with major companies such as JetBrains (IntelliJ) and Apple joining the ecosystem, alongside emerging frameworks like FastMCP. A preview MCP Registry standard now supports public and proprietary tool discovery. However, MCP's rapid evolution has also introduced architectural gaps, drawing criticism for overlooking established RPC best practices. For production applications, teams should look beyond the hype and apply additional scrutiny by mitigating toxic flows using tools like MCP-Scan and closely monitoring the draft authorization module for security.
