Supercharging AI Agents with Azure Context: A Hands-On Guide to Azure MCP
As Large Language Models (LLMs) continue their rapid trajectory of development, software engineers and cloud architects regularly run into two systemic challenges: The…
MCP was pitched as a fix for M*N integration problem, expose your data and tools through MCP once, and any compatible agent can use them.Two things have since kinda changed?1. An agent can now lookup APIs, documentation and call APIs on the fly.
2. MCP's scaling problem seems to have undercut its original promise. If you connect too many servers - it eats up context, so tool loading is deferred or the agent generates code to call a tool, and we are back to square one.I think the best argument is adoption and a bunch of benefits fall out of that - standard auth, API gateway, credential management, all of which could have been done just as well or better, but MCP seems to have won purely because of adoption?What did I miss?
Comments URL: https://news.ycombinator.com/item?id=50388528
Points: 2
# Comments: 0
Discussion (0)
No comments yet. Start the conversation!