Party Time MCP Server

Party Time MCP Server

The Party Time MCP Server is a minimal yet fully‑compliant implementation of the Model Context Protocol (MCP). It answers every request for the current time with a single, fixed response: “It’s Party Time.” While trivial in behavior, the server demonstrates how an MCP endpoint can expose a custom tool () to AI assistants such as Claude, illustrating the mechanics of JSON‑RPC communication, tool registration, and server initialization.

What Problem Does It Solve?

Developers often need a lightweight MCP testbed to validate tool integration, debug protocol handling, or showcase how an assistant can invoke external services. Building a full‑featured server from scratch can be time‑consuming; the Party Time server provides an out‑of‑the‑box example that satisfies all MCP requirements. It allows teams to confirm that their client configuration, tool discovery, and invocation workflows are functioning before moving on to more complex services.

Core Functionality and Value

At its heart, the server registers a single tool named . When this tool is called by an AI assistant, the server responds with a hard‑coded message: “It’s Party Time.” This deterministic output is useful for unit tests, demonstrations, or as a placeholder while a real time‑retrieval service is under development. By handling the full MCP lifecycle—initialization, tool listing, and method invocation—the server validates that an assistant can:

1. Discover available tools via .
2. Request tool execution with .
3. Receive well‑formed JSON responses without extraneous stdout noise.

Because the server writes exclusively to for protocol messages and logs to , it avoids common JSON parsing errors that plague other examples.

Key Features Explained

• Protocol Compliance: Implements , , , and RPC methods, ensuring seamless interaction with any MCP‑capable client.
• Tool Registration: Exposes the tool, illustrating how to define a tool’s name and expected arguments.
• Deterministic Output: Provides a fixed, human‑readable response, making it ideal for testing and demonstrations.
• Robust Logging: Sends diagnostic messages to , keeping the JSON stream clean and preventing parsing failures.
• Cross‑Platform Compatibility: Built with Elixir’s escript tooling, the server runs on any platform that supports standard input/output streams.

Real‑World Use Cases

• Testing Assistant Tool Discovery: Quickly verify that a new assistant can list and call tools without needing a live backend.
• Demonstrations: Show developers or stakeholders how MCP integration works by presenting a simple, predictable tool response.
• Placeholder Service: During iterative development, replace the implementation with a real clock service once the integration is proven.
• Educational Resource: Serve as a teaching aid for courses or workshops on MCP, JSON‑RPC, and AI assistant architecture.

Integration Into AI Workflows

Once configured in an assistant’s client (e.g., Claude Desktop), the server becomes part of the normal tool invocation cycle. When a user asks “What time is it?” the assistant automatically detects the tool, sends a request to the server, and receives the party‑time message. This flow mirrors how real external services—such as weather APIs or database queries—would be accessed, enabling developers to build and test complex workflows without external dependencies.

Standout Advantages

• Simplicity with Full Compliance: A single‑tool server that satisfies every MCP requirement, making it a perfect reference implementation.
• Error‑Free JSON Streams: By separating logs from protocol output, it eliminates a common source of integration bugs.
• Extensibility: The architecture can be expanded to add more tools or richer responses without altering the core protocol handling.

In summary, the Party Time MCP Server is an elegant, minimal example that showcases how to expose a custom tool to AI assistants. It serves as both a practical test harness for developers and an educational model for understanding MCP’s mechanics, making it a valuable asset in any AI‑tool integration pipeline.