MCP Stateful Example Server

Overview

The MCP Stateful Example is a lightweight, fully MCP‑compliant server designed to illustrate how session state can be managed across multiple tool invocations in an AI workflow. By providing a minimal yet complete implementation, it serves as both a learning platform and a validation playground for developers who need to understand or test the intricacies of stateful interactions between an AI assistant and external services.

At its core, the server exposes a standard endpoint that accepts Model Context Protocol requests and returns responses in the expected JSON format. The novelty lies in its session handling: each client request can be associated with a unique session identifier, allowing the server to persist data such as the result of a prior computation. Two toy tools— and —demonstrate this capability. The former generates a random integer within user‑defined bounds and stores it in the session, while the latter retrieves that stored value to compare against a supplied number. This simple dance showcases how state can be leveraged to build more complex, context‑aware toolchains.

Developers benefit from the server’s clear separation of concerns. The directory houses the MCP implementation and a dedicated folder where each tool’s logic is encapsulated. This modularity makes it straightforward to add new tools or modify existing ones without touching the core protocol logic. The accompanying Python integration tests further reinforce confidence in session persistence, providing automated verification that the server behaves as expected across consecutive requests.

In real‑world scenarios, a stateful MCP server is invaluable for tasks that require multi‑step reasoning or iterative refinement. For instance, an AI assistant could first generate a set of candidate solutions (via one tool), then iteratively evaluate each against user constraints using subsequent tools that rely on the previously stored state. Applications in data exploration, automated decision support, or interactive storytelling can all benefit from this pattern.

Overall, the MCP Stateful Example offers a concise yet powerful demonstration of how to integrate session management into an AI‑centric tool ecosystem. Its straightforward API, clear documentation, and built‑in health check make it an excellent starting point for developers looking to prototype or test stateful interactions before scaling to production‑grade services.