/mcp endpoint.
The problem MCPJungle solves
As you add MCP servers to your workflow, the management overhead compounds. Each new server means updating every AI client’s configuration, duplicating credentials, and losing visibility into which tools are being called. When a server moves or changes, you update it in multiple places. MCPJungle centralizes this. You register MCP servers once in the gateway. Every AI client connects to a single endpoint and automatically gets access to all registered tools — no per-client reconfiguration required.How it works
MCPJungle has a client-server architecture:- Gateway server — runs as an HTTP service (default port
8080) and acts as an MCP proxy. It maintains a registry of all your MCP servers, proxies tool calls to the appropriate upstream server, and exposes everything through a single streamable HTTP endpoint at/mcp. The server is typically run via Docker Compose. - CLI client — the
mcpjunglebinary you install locally. Use it to register and deregister MCP servers, manage tool groups, configure access control, and inspect what tools are available.
Who should use MCPJungle?
- Individual developers using Claude Desktop or Cursor who want to access multiple MCP servers without maintaining separate configurations in each client.
- Teams building AI agents that need centralized access control, observability, and a stable endpoint for tool-calling in production.
- Organizations that want to manage all MCP client-server interactions from a single, self-hosted location without sending data to third-party services.
Get started
Quickstart
Start the gateway, register your first MCP server, and connect Claude in under 5 minutes.
Installation
Install the MCPJungle binary via Homebrew, Docker, or a pre-built binary.
Deploy with Docker
Run the MCPJungle server locally or in production using Docker Compose.
Register MCP servers
Add HTTP and STDIO-based MCP servers to the gateway using the CLI.