Is MCP actually reducing integration work for agents?

Lately I’ve been feeling like half of agent development is still just writing connector code over and over again. Every new tool means another wrapper, another schema, another round of debugging because the model formatted something incorrectly. At some point it feels like more time goes into glue code than the actual workflow logic. MCP seems like the first thing that actually makes this cleaner since it standardizes how agents talk to tools and external data sources. The weird part is that a lot of stacks still require manually wiring MCP clients into LangGraph or CrewAI anyway, which kind of defeats the point. I’ve been experimenting with Evose recently because the MCP integration is much more native — you can point the agent at a server/config and inherit the tool access directly instead of building another layer of protocol plumbing yourself. Right now I’ve mostly been using Brave Search, GitHub, and filesystem MCP servers for testing, but I’m curious how many people are already building internal MCP servers for private databases or company tooling. Feels like the ecosystem is moving fast, but most workflows are still stuck in “custom python script for everything” mode.