MCP Demo Server

Overview

The mcpDemo server is a lightweight, transport‑agnostic implementation of the Model Context Protocol (MCP) designed to run on Clever Cloud. Its primary purpose is to illustrate how an MCP server can expose AI‑centric capabilities—such as resources, tools, prompts, and sampling strategies—to external clients without requiring a full‑blown production stack. By using standard input/output () as its communication channel, the demo keeps dependencies minimal while still fully conforming to MCP specifications, making it an ideal starting point for developers who need a quick testbed or proof‑of‑concept before scaling.

For developers building AI assistants that must interact with external services, mcpDemo demonstrates the core workflow: an MCP client sends a request (e.g., to retrieve data, invoke a tool, or prompt a language model) and the server replies with structured JSON that follows MCP’s schema. The demo highlights how a simple Node.js script can act as an MCP endpoint, allowing teams to validate request handling, response formatting, and error management in a controlled environment. This is particularly valuable when integrating with platforms like Clever Cloud, where serverless or containerized deployments are common and where can simplify orchestration.

Key features of the demo include:

• Protocol compliance: Implements the mandatory MCP message types, ensuring that any compliant client can communicate seamlessly.
• Transport flexibility: While the demo uses , the same logic could be adapted to HTTP, WebSocket, or other IPC mechanisms without altering the core MCP logic.
• Developer tooling: The included debugging tool () lets developers inspect live requests and responses, making it easier to troubleshoot protocol mismatches or performance bottlenecks.
• Extensibility hooks: The server skeleton exposes clear points where additional tools, resources, or custom prompts can be added, encouraging rapid iteration.

Typical use cases for this MCP server are:

• Rapid prototyping of AI assistants that need to fetch data from internal APIs or databases.
• Testing MCP clients before deploying them against a production backend, ensuring compatibility and correct error handling.
• Educational demonstrations for teams learning how MCP works, allowing hands‑on experimentation with minimal setup.
• Continuous integration pipelines where a lightweight MCP server can validate new tool integrations or prompt updates automatically.

By integrating mcpDemo into an AI workflow, developers can quickly verify that their client‑side logic aligns with MCP expectations, catch issues early, and iterate on tool definitions. Its minimalistic yet fully compliant design makes it a standout starting point for any project that aims to bridge AI assistants with external services through the Model Context Protocol.