In this tutorial, we build a fully functional MCP-style routed agent system from scratch, combining tool discovery, intelligent routing, structured planning, and execution into a single cohesive workflow. We start by setting up a modular tool server that exposes capabilities such as web search, local retrieval, dataset loading, and Python execution, all defined through structured schemas. We then implement a hybrid router that uses both heuristics and LLM reasoning to dynamically decide which tools to expose for a given task, ensuring minimal yet effective capability exposure. As we progress, we design an agent that plans tool usage, executes calls safely, and synthesizes final answers by injecting context from tool outputs. By the end, we demonstrate multiple real-world tasks and show how MCP principles such as context injection, routing policies, and restricted tool access come together to create a scalable, interpretable, and efficient agent system.
import sys
import subprocess
import pkgutil
def ensure_packages():
