Example MCP Server

The Example MCP Server is a lightweight, reference implementation of the Model Context Protocol (MCP) that demonstrates how an AI assistant such as Claude can be extended with custom, server‑side tools. It solves a common pain point for developers: the need to expose external functionality—like time‑retrieval, database queries, or API calls—to an AI model in a secure, standardized way. By running this server locally and registering it with Claude Desktop, developers can instantly add new capabilities to the assistant without modifying the core AI codebase.

At its core, the server implements the MCP specification by exposing a single “time” tool. When Claude invokes this tool, the server executes a Node.js script that returns the current system time. This simple example showcases how MCP servers can encapsulate any logic, whether it’s a shell command, a Python script, or a microservice. The server listens for JSON‑formatted requests, performs the requested action, and streams back structured responses that Claude can parse and present to users. This pattern allows developers to iterate quickly: write a small script, register it as an MCP server, and immediately make it available to the AI.

Key capabilities of the Example MCP Server include:

• Tool registration: Expose custom commands as named tools that Claude can call by reference.
• Secure execution: The server runs locally, ensuring that sensitive operations stay on the developer’s machine.
• Extensibility: While the demo provides a single time tool, developers can add multiple tools, each with its own command and arguments, simply by extending the configuration.
• Standardized communication: All interactions follow the MCP message format, guaranteeing compatibility with any MCP‑compliant client.

Typical use cases for this server are:

• Custom data retrieval: Fetching values from local databases or files and presenting them to users via the assistant.
• System administration: Running diagnostic commands, monitoring scripts, or automation tasks in response to user queries.
• Prototyping: Quickly testing new tool ideas during development without needing a full backend deployment.
• Secure integrations: Keeping sensitive APIs behind the local server, preventing direct exposure to the AI model.

Integrating the Example MCP Server into an AI workflow is straightforward. After building the server, developers add a configuration entry to Claude Desktop’s settings file, pointing to the compiled JavaScript entry point. Once registered, any prompt that references the “time” tool will trigger a server call, and Claude will display the returned timestamp. This pattern scales: add more tools, update the configuration, and the assistant gains new abilities instantly.

In summary, the Example MCP Server is a practical showcase of how developers can extend AI assistants with custom, local tools. It highlights the simplicity and power of MCP, enabling rapid prototyping, secure integrations, and a clear path to more complex workflows.