A May 15, 2026 tutorial delivers a full, runnable walkthrough for building an MCP‑style routed agent that discovers available tools and selectively exposes capabilities to handle requests. The guide supplies source code and demonstrations that show how the system plans tool use, executes calls, and injects contextual outputs into final responses — a practical reference for developers prototyping multi‑tool LLM agents.
The tutorial walks through assembling a modular tool server that advertises capabilities via structured schemas and examples including web search, local retrieval, dataset loading and Python execution. It pairs that server with a hybrid router that combines heuristics and LLM reasoning to decide which tools to expose for a given request, wiring components together in a single runnable workflow and showing example inputs and outputs.
Implementation details focus on practical guardrails and defaults: the code sets local constants such as MAX_TOOL_CALLS = 3, MAX_WEB_RESULTS = 5 and TOP_K_RETRIEVAL = 3, includes package checks and OpenAI client setup, and demonstrates how to surface only needed capabilities rather than broad permissions. These choices are presented as tradeoffs to balance efficiency, interpretability and safety when agents access external tools.
Framed around MCP principles like context injection, routing policies and restricted tool access, the guide emphasizes interpretable, minimal capability exposure to reduce unnecessary permissions. For practitioners, it functions as a reproducible recipe: installation steps, examples and patterns for synthesizing tool outputs into final answers aim to lower the barrier to prototyping routed agents and adapting the architecture to bespoke toolsets or evaluation tasks.
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