_{Public | Free OpenSource}

# LOTUS-MCP **[FOSS](https://en.wikipedia.org/wiki/Free_and_open-source_software) solution** The LOTUS-MCP protocol outlined here is an impressive approach to **model coordination and processing**, integrating Mistral and Gemini with a structured architecture that allows for: - Routing & fallback strategies between models. - Consensus engine to compare outputs. - Context-aware processing, improving coherence across interactions. - Tool integration, making it extensible for external APIs. - Rate limiting & security for production stability. The Model Context Protocol (MCP) developed by Anthropic for Claude is a groundbreaking open standard that enables AI assistants to connect with external data sources and tools.\ As a developer or business maybe you like to have your own protocol. This guide made for you. First looking into MCP exist by claude: ``` +-------------+ +-------------+ +-------------+ | | | | | | | User | | AI | | External | | Interface |<--->| Model |<--->| Tools | | | |(e.g. Claude)| | & Data | | | | | | | +-------------+ +-------------+ +-------------+ ^ ^ ^ | | | | | | v v v +--------------------------------------------------+ | | | Model Context Protocol | | (MCP) | | | +--------------------------------------------------+ ^ ^ ^ | | | | | | v v v +-------------+ +-------------+ +-------------+ | | | | | | | Development | | Business | | Content | | Environment | | Tools | | Repositories| | | | | | | +-------------+ +-------------+ +-------------+ ``` --- ## Statement Then implement a new modernized structure for MCP. So first thing first is the cost: ``` | Metric | Mistral Target | Gemini Target | |-----------------|----------------|---------------| | Latency | <800ms | <1200ms | | Accuracy | 95% | 92% | | Cost/1k tokens | $0.15 | $0.25 | ``` So to build it we need an architecture design, something like this: ``` ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Decision │ │ │ │ User ├────►│ Router ├────►│ Mistral │ │ Interface │ │ (Task Type │ │ (Code/ │ │ │◄────┤ Analysis) │◄────┤ Text) │ └─────────────┘ └─────────────┘ └─────────────┘ ▲ │ ▲ │ │ └───────┐ │ └────┐ ▼ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ Gemini │ │Fallback │ │Error │ │(Multi- │ │Model │ │Handling │ │ modal) │ │ │ │System │ └─────────┘ └─────────┘ └─────────┘ ``` This is `User Input → Mistral (code/text processing) → Gemini (multimodal enhancement) → Final Output` at the final of our journey we can to build. So go to start: # Beginning our journey Now step-by-step guide to building a unified **Model Context Protocol (MCP)** system for integrating **Mistral** and **Gemini** in one application: - looking [example file](./example.py) , [minimal example](./minimalistExample.py) , [async example](./asyncExample.py) , and [final example](finalExample.py) for learn more about conceptual. --- ## OUR **MCP Architecture Design** ```ascii ┌──────────────┐ ┌───────────────┐ ┌──────────────┐ │ │ │ │ │ │ │ External │ │ Unified │ │ External │ │ Tools │◄─────►│ MCP Server │◄─────►│ Data │ │ (APIs, DBs) │ │ (Orchestrator)│ │ Sources │ └──────▲───────┘ └──────┬───┬────┘ └──────▲───────┘ │ │ │ │ │ ▼ ▼ │ ┌──────┴───────┐ ┌───────────────┐ ┌──────┴───────┐ │ │ │ │ │ │ │ Mistral │ │ MCP Client │ │ Gemini │ │ Interface │◄─────►│(Adapter Layer)│◄─────►│ Interface │ │ │ │ │ │ │ └──────────────┘ └───────────────┘ └──────────────┘ ``` --- ### **1. Protocol Specification** Define your MCP standard with these core components: - **Message Format** (JSON Schema): ```json { "request_id": "uuid", "model": "mistral|gemini|both", "content": {"text": "", "files": []}, "context": {"session": {}, "tools": []}, "routing_rules": {"fallback": "auto", "priority": 0-100} } ``` - **API Endpoints**: - `/mcp/process` - Main processing endpoint - `/mcp/feedback` - Response refinement loop - `/mcp/context` - Session management --- ### **2. Adapter Layer Implementation** Create model-specific adapters that translate MCP protocol to each AI's API: **Mistral Adapter**: ```python class MistralMCPAdapter: def process(self, mcp_request): # Convert MCP format to Mistral's API format mistral_prompt = f"CONTEXT: {mcp_request['context']}\nQUERY: {mcp_request['content']}" response = mistral.generate(mistral_prompt) return self._to_mcp_format(response) def _to_mcp_format(self, raw_response): return { "model": "mistral", "content": raw_response.text, "metadata": { "tokens_used": raw_response.usage, "confidence": raw_response.scores } } ``` **Gemini Adapter**: ```python class GeminiMCPAdapter: def process(self, mcp_request): # Handle multimodal inputs if mcp_request['content']['files']: response = gemini.generate_content( [mcp_request['content']['text'], *mcp_request['content']['files']] ) else: response = gemini.generate_text(mcp_request['content']['text']) return { "model": "gemini", "content": response.text, "metadata": { "safety_ratings": response.safety_ratings, "citation_metadata": response.citation_metadata } } ``` --- ### **3. Unified Processing Workflow** ```python def unified_processing(mcp_request): # Route based on model selection if mcp_request['model'] == 'both': mistral_result = MistralAdapter.process(mcp_request) gemini_result = GeminiAdapter.process(mcp_request) return consensus_engine(mistral_result, gemini_result) elif mcp_request['model'] == 'mistral': return MistralAdapter.process(mcp_request) elif mcp_request['model'] == 'gemini': return GeminiAdapter.process(mcp_request) else: raise MCPError("Invalid model selection") ``` --- ### **4. Context Management System** Implement shared context handling: ```python class MCPContextManager: def __init__(self): self.session_context = {} self.tool_context = { 'database': SQLConnector(), 'apis': [SlackAPI(), GoogleWorkspace()], 'filesystem': S3Storage() } def update_context(self, session_id, new_context): # Maintain 3-level context stack self.session_context[session_id] = { 'immediate': new_context, 'historical': self._rollup_context(session_id), 'persistent': self._load_persistent_context(session_id) } ``` --- ### **5. Tool Integration Layer** Create reusable connectors following MCP standard: ```python class MCPToolConnector: def __init__(self, tool_type): self.tool = self._initialize_tool(tool_type) def execute(self, action, params): try: result = self.tool.execute(action, params) return self._format_mcp_response(result) except ToolError as e: return self._format_error(e) def _format_mcp_response(self, result): return { "tool_response": result.data, "metadata": { "execution_time": result.timing, "confidence": result.accuracy_score } } ``` --- ### **6. Security Implementation** **Authentication Flow**: ``` 1. Client Request ──► MCP Gateway ──► JWT Validation 2. Token Validation ──► Model Access Control List 3. Request Logging ──► Encrypted Audit Trail 4. Response Sanitization ──► Content Filtering ``` **Rate Limiting Setup**: ```python # Use token bucket algorithm for both models mcp_rate_limiter = RateLimiter( limits={ 'mistral': TokenBucket(rate=100/60), # 100 requests/minute 'gemini': TokenBucket(rate=50/60), 'combined': TokenBucket(rate=75/60) } ) ``` --- ### **7. Deployment Strategy** **Recommended Stack**: ```yaml services: mcp_gateway: image: nginx-plus config: rate_limiting: enabled core_service: image: python:3.11 components: - model_adapter_layer - context_manager - tool_connectors monitoring: stack: prometheus + grafana metrics: - model_performance - context_hit_rate - tool_usage ``` --- ### **8. Testing Framework** Implement 3-level verification: 1. **Unit Tests**: Individual adapters and connectors 2. **Integration Tests**: Full MCP request flows 3. **Chaos Tests**: Model failure simulations Example test case: ```python def test_cross_model_processing(): request = { "model": "both", "content": "Explain quantum computing in simple terms", "context": {"user_level": "expert"} } response = unified_processing(request) assert 'mistral' in response['sources'] assert 'gemini' in response['sources'] assert validate_consensus(response['content']) ``` --- ## Key Advantages of This Approach 1. **Unified Interface**: Single protocol for both models 2. **Context Sharing**: Maintains session state across different AI systems 3. **Tool Reusability**: Common connectors work with both Mistral and Gemini 4. **Cost Optimization**: Smart routing based on model capabilities 5. **Failover Support**: Automatic fallback between models Start with implementing the adapter layer first, then build out the context management system before adding tool integrations. Use gradual rollout with shadow mode (run both models but only show one output) to compare performance before full deployment. 💐 **Congratulations, you own your own MCP-like framework!** 🍷 --- **Disclaimer**: The codes may not ultimately produce real results, this is just a workaround. Understand the path architecture and build the foundation for this movement in the world of AI. **Licenses**: MIT , Apache 2 — So feel free to use & edit & distribution. **credit**: [Blue Lotus](https://lotuschain.org)