Scaled MCP Server

The Scaled MCP Server tackles a common pain point for developers building AI‑powered applications: the need to expose rich, structured toolsets and session data to an assistant while maintaining high availability and performance. Traditional single‑node MCP implementations struggle when traffic spikes or when multiple services must share state, leading to bottlenecks and unreliable user experiences. This server implements the 2025‑03 MCP specification with horizontal scaling built in, allowing it to run behind a load balancer and distribute requests across many instances without losing session continuity or tool state.

At its core, the server is a lightweight Go library that can be embedded directly into any application. It exposes a standard HTTP endpoint () and an optional Server‑Sent Events (SSE) channel for real‑time streaming, making it straightforward to integrate with existing web stacks. The API follows the MCP contract: clients discover available resources, negotiate capabilities, and invoke tools through structured JSON messages. By adhering strictly to the spec, developers can swap out different assistants or tooling backends without changing client code.

Key capabilities include:

• Distributed Session Management – Sessions can be stored in Redis or kept in memory for simple setups, ensuring that conversation context is preserved across multiple server nodes.
• Actor‑Based Architecture – Each session runs as an isolated actor, handling message routing and tool execution independently. This isolation improves fault tolerance and simplifies scaling logic.
• Tool Registry – A flexible registry lets developers register static or dynamic tools, attach custom handlers, and define rich input schemas. The example calculator tool demonstrates how to expose arbitrary functionality as a first‑class MCP resource.
• Load‑Balanced Deployment – The server is designed to work behind a reverse proxy or load balancer. Requests are evenly distributed, and session affinity is managed transparently through the chosen store.

In practice, Scaled MCP shines in scenarios such as enterprise chatbots that need to perform database queries, compute analytics, or invoke microservices while maintaining state across thousands of concurrent users. It also fits well into DevOps pipelines where multiple assistants must share a common toolset, or in multi‑tenant SaaS platforms that require isolated sessions per customer. By providing a robust, spec‑compliant backbone, the server frees developers to focus on crafting intelligent prompts and tool logic rather than wrestling with networking or state management.