MCP Server Demo

The Mcp Server Demo is a lightweight, TypeScript‑based showcase that brings the Model Context Protocol (MCP) into a real‑world setting. It demonstrates how an AI assistant—such as Claude Desktop—can be extended with custom tools, dynamic resources, and natural‑language prompt mappings without any deep infrastructure changes. By packaging these capabilities behind a simple HTTP server, developers can quickly prototype and iterate on AI‑powered workflows that rely on external data or actionable commands.

At its core, the server solves a common pain point: how to give an LLM direct, type‑safe access to external services while keeping the integration logic isolated from the model itself. The MCP framework standardizes this interaction, allowing the assistant to discover available tools and resources through a well‑defined schema. The demo shows that with just a few lines of TypeScript, you can expose any function as an LLM‑callable tool and serve real‑time data via URLs, all while maintaining strict type safety and clear documentation.

Key features of the demo include:

• Custom Tool Registration – Functions are wrapped with metadata (name, description, parameters) so the LLM can invoke them seamlessly.
• Dynamic Resource Serving – Endpoints return live data (e.g., JSON from an API) that the model can consume on demand.
• Prompt Mapping – Natural‑language prompts are linked to specific tools, enabling the assistant to interpret user intent without hardcoding every scenario.
• Cross‑Provider Flexibility – Because MCP is agnostic to the underlying LLM, developers can switch between Claude, OpenAI, or any other provider without touching the server code.
• Security and Isolation – All tool logic runs on the server side, keeping sensitive credentials or business logic out of the model’s training data.

In practice, this server is ideal for building agent‑style applications where an LLM needs to perform tasks such as querying a database, calling third‑party APIs, or executing internal business logic. For example, an e‑commerce chatbot could use the server to look up inventory levels or calculate shipping costs in real time, while a data‑analysis assistant could pull fresh metrics from a dashboard. Because the MCP specification handles routing and serialization, developers can focus on crafting useful tools rather than wrestling with low‑level networking.

What sets this demo apart is its simplicity and extensibility. It acts as both a learning resource for newcomers to MCP and a production‑ready template that can be scaled with additional services or integrated into larger microservice architectures. By exposing a clean, well‑documented API surface, it encourages collaboration between AI developers and domain experts, ensuring that the assistant’s capabilities evolve in sync with business needs.