iflow-mcp_super-i-tech_mcp_plexus

# MCP Plexus: Secure, Multi-Tenant MCP Server Framework for Modern AI Build powerful, scalable, and secure Model Context Protocol (MCP) applications with ease. MCP Plexus is a Python framework built on the robust `jlowin/fastmcp` (FastMCP 2.7) library, designed to empower developers to deploy multi-tenant MCP servers that seamlessly integrate with external services via OAuth 2.1 and manage API key access for tools. --- ## Introduction ### What is MCP Plexus? MCP Plexus extends the capabilities of FastMCP 2.7, providing a structured environment for creating sophisticated, multi-tenant AI backend systems. It allows you to define distinct, isolated environments (tenants) that can expose customized sets of tools, resources, and prompts to Large Language Models (LLMs) and AI agents. **Key differentiating features include:** - **Robust Multi-Tenancy:** Host multiple clients or organizations on a single deployment, each with their own data and tool access. - **Simplified External Service Integration:** Securely connect your MCP tools to external OAuth 2.1 protected services (like GitHub, Google APIs, etc.) with built-in flow management. - **User-Specific Persistent Access:** Enable host applications to register their users with Plexus, allowing for persistent storage of external OAuth tokens, reducing re-authentication friction. - **API Key Management for Tools:** Securely store and inject API keys for tools that require direct key-based authentication to external services. - **Standardized and Extensible:** Leverages the full power of FastMCP's Pythonic decorators for defining MCP components, while providing a clear structure for extension. ### Why MCP Plexus? As AI applications grow in complexity, the need to securely provide LLMs with relevant context and executable capabilities becomes paramount. MCP Plexus addresses this by: - **Simplifying Multi-Tenant Deployments:** Reduces the boilerplate and complexity of managing multiple, isolated MCP environments. - …