MCP has a BIG problem

MCP has several challenges but the biggest one is the fact that it is a stateful protocol. This effectively means that you cannot add MCP compatibility to a simple REST API - you need to deploy a stateful long-running server. This also means that serverless functions - which are a natural fit for AI tool calls - cannot be used with MCP. I believe this is a show-stopper for MCP and that other simpler methods are the best way forward for solving the AI data integration problem. Ultimately, we need to hook up existing APIs to Agents - not force developers to create and deploy new types of servers. 📚 Resources: My previous MCP video:    • MCPs explained (and how to build one)   My written thoughts on MCP state: https://github.com/modelcontextprotoc... MCP state full discussion: https://github.com/modelcontextprotoc... MCP sampling: https://modelcontextprotocol.io/docs/... Wild-card Agents json: https://docs.wild-card.ai/agentsjson/... 🚀 In This Video, You'll learn: What are MCPs The biggest issue with MCP MCP alternatives What is Agents.json OpenAPI vs MCP Agents.json vs MCP MCPs for beginners Limitations of MCP Can MCP be used in serverless functions 💡 Perfect for Viewers Interested in: AI data integration AI agent workflows Building MCPs Software Development 2025 Using AI to code Coding with AI Latest AI tutorials 🔴 Subscribe for more tutorials on AI and programming! Chapters: 00:00 - The Big Problem with MCP 03:00 - Why does MCP need state? 06:31 - MCP Alternatives 07:44 - A thought on Agent Tool Routing