How to Build an MCP Style Routed AI Agent System with Dynamic Tool Exposure Planning, Execution, and Context Injection

This tutorial builds a full MCP-style routed agent system from scratch, combining tool discovery, intelligent routing, structured planning, and execution. It features a modular tool server exposing web search, local retrieval, dataset loading, and Python execution; a hybrid router that dynamically selects tools using heuristics and LLM reasoning; and an agent that plans tool usage, executes calls safely, and synthesizes answers by injecting context from tool outputs. Real-world tasks demonstrate how MCP principles like context injection, routing policies, and restricted tool access create a scalable, interpretable, and efficient agent system.