Making a Local LLM MCP Server Deterministic: Model Routing, Think-Block Stripping, and the Problems Nobody Warns You About

For some time, I've been experimenting with the idea that by using an MCP server, we can delegate bounded tasks from Claude Code to cheaper local or cloud models (models I run on a local server in LM Studio). It makes sense, why chew through long, repetitive regression testing tasks when this could be directed by claude, but executed by a simpler, arguably more efficient for the task model instead? The other worry I have - what if Anthropic added a few zeros to their subscription and half of us had to rethink how we use the flagship models? This is my ongoing experiment. There's no "this is how you have to work from now on" pressure that I feel everytime I read about a new release, I'm just curious to see if we can get to a point where Claude is orchestrating and delegating to whatever local model(s) you have available for the same of token efficiency. It might matter one day! My v1 was simple - running one model, on one endpoint, instructing Claude to think about handover for specific