MCP's Biggest Growing Pains for Production Are About to Be Solved
The Model Context Protocol has had a remarkable year. What started as Anthropic’s attempt to standardize how AI models connect to external tools and data sources has become, almost by accident, the de-facto tool layer for the entire agentic AI ecosystem. Claude uses it. OpenAI-compatible agents use it. Builders across the industry are shipping MCP servers like it’s the new API endpoint. But if you’ve tried to run MCP seriously in production, you’ve bumped into the same set of friction points. Authentication is awkward. Streaming is limited. Discovering MCP servers requires manual configuration. Multi-agent handoffs lack proper task lifecycle semantics. And when things fail — network blips, agent restarts, timeout conditions — the retry behavior is undefined. ...