Minimind Org MCP Server – TypeScript Template

The Minimind Org MCP Server is a lightweight, ready‑to‑use foundation for building Model Context Protocol (MCP) servers in TypeScript. It demonstrates how an external service can expose custom tools to AI assistants such as Claude, enabling those assistants to perform domain‑specific actions directly from the model’s prompt. For developers looking to add programmable functionality to their AI workflows, this template removes boilerplate and gives a clear example of the core MCP concepts—resources, tools, prompts, and sampling—in a modern, type‑safe environment.

Problem Solved

AI assistants today can process natural language and generate code, but they lack a standardized way to invoke external logic or access third‑party data. Existing solutions often require custom adapters or ad‑hoc HTTP calls, leading to fragile integrations and inconsistent security models. The MCP framework solves this by defining a simple, language‑agnostic protocol that both the model and the server understand. The Minimind template brings this power to TypeScript developers, allowing them to expose fully typed tools that the assistant can call as if they were built‑in functions.

Core Functionality

At its heart, the server implements two illustrative tools:

1. Example Operation – a placeholder that demonstrates how to structure a tool, including request validation and response formatting.
2. Addition – a concrete calculator that takes two numbers and returns their sum.

These tools showcase the essential MCP workflow: the assistant sends a structured request, the server validates input against TypeScript types, performs the operation, and returns a JSON payload that the assistant can incorporate into its output. The server also includes robust error handling and utility modules, ensuring that any unexpected conditions are communicated back to the model in a consistent manner.

Key Features

• Type‑Safe Tool Definitions – Every operation is defined with explicit TypeScript interfaces, guaranteeing that both the client and server agree on input and output shapes.
• Modular Project Structure – Separate folders for constants, errors, types, and utilities keep the codebase clean and extensible.
• SDK Integration – The server leverages the official MCP SDK for all protocol handling, abstracting away low‑level networking details.
• Extensible Operations – Adding a new tool is as simple as creating a new file in the directory and registering it in the main server entry point.

Use Cases

• Custom Calculations – Embed domain‑specific arithmetic or statistical functions that the assistant can call on demand.
• API Wrappers – Wrap REST or GraphQL services behind a tool, allowing the model to query external data without leaving the prompt.
• Workflow Orchestration – Chain multiple tools to perform complex tasks, such as fetching data, transforming it, and generating a report—all orchestrated by the assistant.
• Secure Execution – Run sensitive operations in a controlled server environment, ensuring that only authenticated models can invoke them.

Integration with AI Workflows

Developers integrate the server by configuring their MCP‑enabled client (e.g., Claude Desktop) to point at the compiled executable. Once registered, the assistant automatically discovers the available tools during a conversation and can invoke them with natural language prompts. The server’s responses are then seamlessly merged into the model’s output, providing a fluid experience where code, data, and logic coexist in a single dialogue.

The Minimind Org MCP Server TypeScript Template thus offers a clean, type‑safe starting point for any developer who wants to expose custom functionality to AI assistants. By following the patterns in this template, teams can rapidly build secure, maintainable tools that enrich AI workflows and unlock new use cases.