servers

MCP itself doesn't require the use of the LLM. There are other concepts, but for this use, Tools are key. A Tool is an operation, like a search. Have a look at the Filesystem example MCP server - https://github.com/modelcontextprotocol/servers/blob/main/sr.... It has a collection of Tools - read_file, read_multiple_files, write_file, etc. The LLM uses MCP to learn what tools the server makes available; the LLM decides when to use them. (The process is a little more complicated than that, but if you're just trying to call tools without an LLM, those parts aren't really important.) If you ask the LLM, "Find all files with an asterisk," it might deduce that it can use the search_files tool provided by that MCP to gain the needed context, invoke that tool, then process the results it returns. As an engineer, you can just call search_files if

You don't need to aspire to use "MCP", but rather select some MCP servers that will be useful for you. MCP servers basically expose pieces of the web/data that your LLM client has not been trained on. Maybe some private APIs, or internal data that isn't part of training data sets. Real-time data is another good use case. This list has a lot of ideas: https://github.com/modelcontextprotocol/servers?tab=readme-o... I personally use the Kagi MCP server (allows an LLM client to execute searches against the Kagi search engine), and stochastic thinking/sequential thinking (some alternate thought patterns that help models escape the "valley of optimization" problem). You can't tell Claude.md to "use the Kagi search engine"...it doesn't have an API key to do that. The MCP server basically just exposes the API. You're right, it's a lot less exciti