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: https://youtu.be/m46tZX6vceI. – My written thoughts on MCP state: https://github.com/modelcontextprotocol/specification/discussions/102#discussioncomment-12445025. – MCP state full discussion: https://github.com/modelcontextprotocol/specification/discussions/102 . – MCP sampling: https://modelcontextprotocol.io/docs/concepts/sampling. – Wild-card Agents json: https://docs.wild-card.ai/agentsjson/schema#top-level-structure. . 🚀 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