mcpjungle/MCPJungle

What it solves

MCPJungle solves the complexity of managing multiple Model Context Protocol (MCP) servers. Instead of configuring every AI client (like Claude, Cursor, or Copilot) to connect to each server individually, MCPJungle acts as a centralized gateway, providing a single endpoint for all registered tools, prompts, and resources.

How it works

It operates as a self-hosted gateway with a client-server architecture. Users register their MCP servers—supporting both STDIO and Streamable HTTP transports—via a CLI or JSON configuration. The MCPJungle server then proxies requests from AI clients to the appropriate upstream servers. It can be deployed locally via Docker or as shared infrastructure for teams, utilizing SQLite or PostgreSQL for state management.

Who it’s for

Developers and teams who use multiple MCP servers across various AI clients and want to avoid duplicated setup, scattered configurations, and inconsistent access control.

Highlights

• Unified Endpoint: Connects multiple AI clients to a single MCP gateway.
• Centralized Management: One place to register, manage, and discover tools, prompts, and resources.
• Flexible Transport Support: Compatible with STDIO and Streamable HTTP MCP servers.
• Deployment Options: Supports local Docker setups or enterprise-grade deployments with PostgreSQL.
• Stateful Connections: Option to maintain persistent connections to upstream servers to reduce latency (solving the "cold-start" problem).
• Access Control: Includes hooks for observability and access control in shared team environments.