What installation methods are available for Foxxy Bridge?

You can install Foxxy Bridge conveniently using `uv` (recommended), directly from its GitHub repository, or by running it as a Docker container.

How do I integrate Foxxy Bridge with my AI tools?

Point your MCP-compatible AI client to the bridge's single endpoint (e.g., `http://localhost:8080/sse`), and it will transparently route requests to the appropriate backend server.

What is MCP Foxxy Bridge?

MCP Foxxy Bridge acts as a one-to-many proxy for the Model Context Protocol (MCP), aggregating and routing requests from a single endpoint to multiple MCP servers.

How does Foxxy Bridge simplify AI development?

It centralizes MCP server configuration, exposes all tools and prompts from connected servers, and allows AI development tools like Claude Desktop and VS Code to use a single MCP endpoint.

Foxxy Bridge

Name: Foxxy Bridge
Author: billyjbryant

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Aggregates and routes requests to multiple Model Context Protocol (MCP) servers through a single endpoint.

About

Foxxy Bridge acts as a central proxy for the Model Context Protocol (MCP), enabling users to consolidate and manage multiple MCP servers under one unified access point. It streamlines the integration of various MCP-compatible tools and resources by centralizing their configuration and exposing them through a single endpoint, simplifying setup for AI tools like Claude Desktop or VS Code.

Key Features

Utilize a single MCP endpoint in AI development tools
Centralize configuration for multiple MCP servers
Expose all tools, resources, and prompts from connected servers
Support for various installation methods including uv and Docker
5 GitHub stars
Transparently route requests to the correct backend server

Use Cases

Streamlining developer workflow by providing a single access point for all Model Context Protocol interactions.
Simplifying the integration of multiple MCP-compatible services into a single AI application.
Managing and consolidating diverse MCP servers (e.g., for GitHub, filesystem, web fetching) from a unified point.

About

Key Features

Utilize a single MCP endpoint in AI development tools
Centralize configuration for multiple MCP servers
Expose all tools, resources, and prompts from connected servers
Support for various installation methods including uv and Docker
5 GitHub stars
Transparently route requests to the correct backend server

Use Cases

Streamlining developer workflow by providing a single access point for all Model Context Protocol interactions.
Simplifying the integration of multiple MCP-compatible services into a single AI application.
Managing and consolidating diverse MCP servers (e.g., for GitHub, filesystem, web fetching) from a unified point.