LOTUS-MCP
by blue-lotus-org
LOTUS-MCP is a FOSS solution for model coordination and processing, integrating Mistral and Gemini with a structured architecture. It provides a guide to build your own Model Context Protocol (MCP)-like framework.
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What is LOTUS-MCP?
LOTUS-MCP is a guide and framework for building a Model Context Protocol (MCP) system that integrates different AI models like Mistral and Gemini. It defines a structured architecture for routing, fallback strategies, consensus, context-aware processing, tool integration, and security.
How to use LOTUS-MCP?
The guide provides step-by-step instructions on building a unified MCP system, including defining the protocol specification, implementing adapter layers for each AI model, creating a unified processing workflow, managing context, integrating tools, implementing security measures, and deploying the system. Example files are provided for learning more about the conceptual implementation.
Key features of LOTUS-MCP
Unified Interface for multiple models
Context Sharing across different AI systems
Reusable Tool Connectors
Cost Optimization through smart routing
Failover Support with automatic fallback
Rate limiting & security for production stability
Consensus engine to compare outputs
Use cases of LOTUS-MCP
Building AI assistants that can leverage multiple models for different tasks
Creating a system that can automatically switch between models based on cost and performance
Developing a platform that can integrate with external data sources and tools
Implementing a secure and scalable AI infrastructure
Enhancing AI application with multimodal capabilities
FAQ from LOTUS-MCP
What is the purpose of the MCP?
What is the purpose of the MCP?
The Model Context Protocol (MCP) enables AI assistants to connect with external data sources and tools, providing a structured way to manage context and integrate different AI models.
What models does LOTUS-MCP support?
What models does LOTUS-MCP support?
The guide focuses on integrating Mistral and Gemini, but the architecture can be adapted to support other AI models as well.
What are the core components of the MCP standard?
What are the core components of the MCP standard?
The core components include a message format (JSON Schema), API endpoints (/mcp/process, /mcp/feedback, /mcp/context), and model-specific adapters.
How does LOTUS-MCP handle context management?
How does LOTUS-MCP handle context management?
The MCPContextManager maintains a 3-level context stack, including immediate, historical, and persistent context, to ensure coherence across interactions.
What security measures are implemented in LOTUS-MCP?
What security measures are implemented in LOTUS-MCP?
Security measures include JWT validation, model access control lists, encrypted audit trails, content filtering, and rate limiting using a token bucket algorithm.