Scaled MCP
Scaled MCP is a high-performance, horizontally scalable Message Context Protocol (MCP) server designed for AI applications. It supports load balancing, session management, and flexible configuration, enabling seamless deployment across multiple nodes. Its actor-based architecture facilitates efficient message routing and session handling, making it suitable for large-scale AI services requiring reliable message exchange and scalability.
Added on:
Created by:
Apr 27 2025
Scaled MCP is a high-performance, horizontally scalable Message Context Protocol (MCP) server designed for AI applications. It supports load balancing, session management, and flexible configuration, enabling seamless deployment across multiple nodes. Its actor-based architecture facilitates efficient message routing and session handling, making it suitable for large-scale AI services requiring reliable message exchange and scalability.
Added on:
Created by:
Apr 27 2025
Traego
Featured
What is Scaled MCP?
Scaled MCP is an advanced MCP and A2A server implementation that enables developers to build scalable AI communication platforms. It adheres to MCP 2025-03 standards and offers features such as load-balanced deployment, session management via Redis or in-memory options, and an actor system for efficient message routing. Its support for HTTP transport, SSE, and external router integration allows flexible deployment in various environments. Ideal for AI chatbots, distributed AI systems, and enterprise AI solutions, it ensures reliable and scalable message processing across multiple nodes. Its modular design and open-source license facilitate customization and integration into complex AI architectures.
Who will use Scaled MCP?
- AI developers
- Distributed system architects
- Research institutions working on large-scale AI
- enterprises deploying scalable AI solutions
- Open-source contributors in the AI ecosystem
How to use the Scaled MCP?
- Step 1: Install the MCP server library using 'go get github.com/traego/scaled-mcp'.
- Step 2: Configure the server settings, including session management and transport options.
- Step 3: Define and register tools and capabilities the server will offer.
- Step 4: Initialize the server with your configuration and registered tools.
- Step 5: Start the server and connect your clients or external HTTP servers.
- Step 6: Monitor, scale, and manage the server as needed for your AI deployment.
Scaled MCP's Core Features & Benefits
The Core Features
- HTTP transport with capabilities negotiation
- Distributed session management with Redis or in-memory
- Actor-based message routing system
- Horizontal scaling for load balancing
- Support for external routers and custom endpoints
The Benefits
- Supports large-scale, distributed AI applications
- Flexible deployment with external HTTP routers
- Reliable session management across multiple nodes
- Efficient message handling via actor architecture
- Open-source with customization options
Scaled MCP's Main Use Cases & Applications
- Building distributed AI chatbots with load balancing
- Enterprise AI communication platforms
- Large-scale research projects requiring message synchronization
- Multi-node AI service deployment
- AI message routing and session management in cloud environments
FAQs of Scaled MCP
Developer
- Traego
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