Rust MCP Tutorial – A Quick‑Start Model Context Protocol Server

The Rust MCP Tutorial is a lightweight example of an MCP (Model Context Protocol) server written in Rust. It demonstrates how to expose a minimal set of capabilities—such as resources, tools, prompts, and sampling—to an AI client like Claude. By following this tutorial, developers can see how a Rust‑based MCP server is structured and learn the essential steps to extend it for real applications.

This server solves a common pain point for AI‑centric developers: bridging the gap between an LLM’s conversational interface and external services or data sources. Instead of hard‑coding custom APIs for each tool, MCP provides a standard, language‑agnostic contract that any LLM can consume. The Rust implementation showcases how to implement this contract efficiently, leveraging Rust’s safety guarantees and performance while keeping the codebase concise.

Key capabilities of the tutorial server include:

• Resource management: Exposes a simple in‑memory store that can be queried or updated through the MCP API, illustrating how stateful data can be shared between a client and server.
• Tool invocation: Implements a mock tool that performs basic computations (e.g., arithmetic or string manipulation). This demonstrates the “tool” pattern in MCP, where a client can request deterministic actions and receive structured results.
• Prompt templating: Provides a basic prompt engine that can inject dynamic values into templates before sending them to the LLM. This is useful for templated responses or context‑specific queries.
• Sampling control: Exposes sampling parameters (temperature, top‑k, etc.) that the client can tweak on the fly, giving developers fine‑grained control over the LLM’s output style.

In real‑world scenarios, such a server can be integrated into workflow automation pipelines. For example, an AI assistant could query the resource store to fetch user preferences, invoke a tool to calculate shipping costs, and then format the response using a prompt template before presenting it back to the user. The MCP contract ensures that each component—data, tool logic, and LLM prompts—remains decoupled yet interoperable.

What sets this tutorial apart is its focus on clarity and extensibility. The code is deliberately minimal, yet it follows best practices for Rust web services (async handling, structured logging, and error propagation). Developers can use it as a foundation to build production‑grade MCP servers that interface with databases, external APIs, or custom inference engines. By adopting the MCP standard, teams can rapidly iterate on AI workflows without reinventing communication protocols for each new tool or data source.