LOTUS-MCP
FOSS solution
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:
OUR MCP Architecture Design
┌──────────────┐ ┌───────────────┐ ┌──────────────┐
│ │ │ │ │ │
│ 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):
{
"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 :
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 :
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
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:
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:
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 :
# 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 :
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:
- Unit Tests : Individual adapters and connectors
- Integration Tests : Full MCP request flows
- Chaos Tests : Model failure simulations
Example test case:
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
- Unified Interface : Single protocol for both models
- Context Sharing : Maintains session state across different AI systems
- Tool Reusability : Common connectors work with both Mistral and Gemini
- Cost Optimization : Smart routing based on model capabilities
- 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
