AI Agent for mcp-proxy
Seamlessly integrate mcp-proxy to bridge stdio and SSE/StreamableHTTP protocols, enabling smooth communication between local and remote servers and unlocking new automation possibilities for tools like Claude Desktop and LLM clients. Simplify server transport switching and enhance your workflow reliability with robust proxy management.
Switch Between stdio and SSE/StreamableHTTP
Effortlessly proxy between stdio and remote SSE/StreamableHTTP servers. Enable clients like Claude Desktop to communicate with remote servers even when native support is absent. Achieve reliable, low-latency transport switching for your AI and LLM workflows.
- Bidirectional Proxy Modes.
- Supports both stdio-to-SSE and SSE-to-stdio, allowing flexible integration between clients and servers.
- Remote Server Connectivity.
- Connect local tools to remote servers via SSE/StreamableHTTP even if native support is missing.
- Customizable Transport Options.
- Choose between SSE or StreamableHTTP, with configurable headers, tokens, and environment variables.
- Low-Latency Communication.
- Ensures fast and reliable data transfer between clients and servers.
Flexible Configuration & Easy Deployment
Deploy mcp-proxy seamlessly using PyPI, Smithery, GitHub, or Docker. Customize your connection with simple arguments or JSON config files, supporting named servers and easy updates for enterprise AI workflows.
- Multiple Installation Options.
- Install via PyPI, Smithery, Docker, or directly from GitHub for ultimate flexibility.
- Simple JSON & CLI Configuration.
- Configure endpoints, headers, and environment variables through easy-to-manage files or CLI arguments.
- Named Server Management.
- Define and access multiple named servers for advanced routing and multi-backend support.
Robust Management & Extensibility
Monitor global status, extend container images, and troubleshoot with detailed logs. mcp-proxy provides reliable status endpoints and supports custom server setups for scalable AI infrastructure.
- Status Endpoints.
- Monitor global server health and status via dedicated endpoints.
- Container Extensibility.
- Extend and customize Docker images for tailored deployment in any environment.
- Easy Troubleshooting.
- Comprehensive logs and documentation for fast issue resolution.
Connect Your mcp-proxy Integration with FlowHunt AI
Connect your mcp-proxy Integration to a FlowHunt AI Agent. Book a personalized demo or try FlowHunt free today!
What is mcp-proxy
mcp-proxy is an open-source tool developed to serve as a bridge between Streamable HTTP and stdio MCP (Multi-Channel Protocol) transports. It enables seamless switching between standard input/output (stdio) communication and Server-Sent Events/Streamable HTTP, making it possible to use MCP-compatible servers and clients across different transport protocols. This is particularly valuable for developers and enterprises seeking to unify or modernize their communication flows between AI agents, backend services, and other infrastructure. The project is hosted on GitHub and is designed for high flexibility, supporting both stdio to SSE/StreamableHTTP and SSE to stdio conversion modes, making integration with modern AI and automation stacks straightforward.
Capabilities
What we can do with mcp-proxy
mcp-proxy allows users to flexibly connect and operate between different MCP server transports. Its main features enable protocol bridging, integration, and easy configuration for advanced server/client setups.
- Bridge stdio and HTTP
- Seamlessly convert between stdio and Streamable HTTP transports for MCP-based services.
- Centralize communication
- Aggregate multiple MCP resource servers through a single proxy interface.
- Enhance interoperability
- Facilitate integration of legacy MCP clients with modern web-based infrastructures.
- Flexible configuration
- Easily set up and manage transport modes and server connections via config files.
- Open-source & extensible
- Leverage community-driven development for customization and enterprise adoption.
How AI agents benefit from mcp-proxy
AI agents and automation systems can leverage mcp-proxy to standardize communication across diverse infrastructure, enabling robust protocol translation, reducing integration complexity, and making scalable deployment easier. This ensures AI agents can interact efficiently with various backend systems regardless of their native transport, promoting interoperability, reliability, and streamlined orchestration.