Jailbreak-MCP Server

Overview

The Hyper Mcp server, also known as jailbreak-mcp, is designed to bridge the gap between AI assistants and the full breadth of the Model Context Protocol (MCP). While many MCP clients today only expose a subset of the protocol—typically just tools—the Hyper Mcp server transforms all configured resources, prompts, and other capabilities into callable tools. This means even the most restrictive client implementations can fully leverage a server’s rich feature set without needing native support for every MCP concept. The result is a single, lightweight server that delivers the complete MCP experience to any assistant, regardless of its built‑in limitations.

Hyper Mcp solves a practical problem that emerges when working with multiple MCP servers: the rapid exhaustion of an assistant’s context window. Every tool, prompt, or resource consumes a portion of the limited token budget. When an assistant pulls in dozens of tools from several servers, the window shrinks to the point where meaningful dialogue becomes impossible. Hyper Mcp addresses this by introducing modes—configurable whitelists that expose only the exact subset of tools, prompts, and resources needed for a particular task. Developers can switch modes on the fly using dedicated tools, ensuring that the assistant’s context remains lean and focused while still providing access to all required functionality.

Key capabilities of Hyper Mcp include:

• Full protocol support: Exposes tools, resources, prompts, and sampling settings as standard MCP endpoints.
• Mode-based tool gating: Allows fine‑grained control over which capabilities are active, preventing context overflow and enabling dynamic workflow switching.
• Cross‑client compatibility: Works seamlessly with popular MCP clients such as Cursor, Claude Desktop, and Cherry Studio, even if those clients lack native support for resources or prompts.
• Extensibility: Configurable via a simple JSON file, making it easy to add new tools or update existing ones without touching the server code.

Real‑world use cases abound. A data analyst might create a “data‑prep” mode that exposes only CSV parsing and statistical tools, while a customer support bot could switch to a “ticket‑resolution” mode that unlocks knowledge base prompts and ticket‑creation resources. In research settings, a scientist could toggle between “simulation” and “visualization” modes to keep the assistant’s context focused on the current experiment. By encapsulating complex tool sets behind lightweight modes, Hyper Mcp lets developers craft highly specialized AI workflows that remain performant and responsive.

In summary, Hyper Mcp turns the Model Context Protocol from a theoretical standard into a practical, developer‑friendly engine. It eliminates protocol gaps in existing clients, protects context windows through intelligent mode management, and unlocks the full power of MCP‑enabled tools for any AI assistant workflow.