MCP is really a great leap because LLMs orchestrate across a collection of tools instead of running a scripted flow. The most obvious example is deep research, where the LLM sends initial queries, reads, then generates new queries and loops until it finds what it needs. This dynamic orchestration of the search tool is almost impossible to do in a scripted way. And it shows where the MCP value is - you just write simple tools, and AI handles the contextual application. You just make the backend, the front end is the LLM with human in the loop.

I made an MCP with 2 tools - generate_node and search, and with it Claude Desktop app can create a knowledge graph complete with links and everything. It scales unbounded by context size but is read/write and smarter than RAG because it uses graph structure not just embeddings. I just made the reading and writing tools, the magic of writing the nodes, linking them up, searching and analyzing them is due to AI. And again, Claude can be very efficient at wielding these tools with zero effort on my part. That is the value of MCP.