AI Integration Overview

MockLoop MCP is specifically designed to enhance AI-assisted development workflows, providing seamless integration with popular AI frameworks and enabling sophisticated testing and development scenarios.

Why AI Integration?

Modern AI development requires robust testing environments, realistic data, and the ability to simulate various scenarios. MockLoop MCP addresses these needs by:

• Providing Realistic APIs: Generate mock servers that behave like real services
• Enabling Dynamic Testing: Create and switch between test scenarios instantly
• Offering Comprehensive Analytics: Monitor AI system interactions with detailed logging
• Supporting Rapid Iteration: Update responses and configurations without restarts

Supported AI Frameworks

MockLoop MCP provides native integration with major AI frameworks:

🔗 LangGraph

• Workflow Integration: Embed mock servers in LangGraph state machines
• Dynamic State Management: Update mock responses based on workflow state
• Error Simulation: Test error handling and recovery scenarios
• Performance Testing: Simulate API latencies and failures

👥 CrewAI

• Multi-Agent Testing: Provide different mock services for different agents
• Role-Based Scenarios: Create scenarios tailored to specific agent roles
• Collaborative Workflows: Test agent interactions through mock APIs
• Task Validation: Verify agent outputs against expected API responses

🦜 LangChain

• Tool Integration: Use MockLoop tools within LangChain applications
• Chain Testing: Test complex chains with realistic API responses
• Memory Validation: Verify chain memory against mock data
• Retrieval Testing: Mock external data sources for RAG applications

🔧 Custom Frameworks

• Generic Integration: REST API compatibility with any framework
• Custom Tools: Extend MockLoop with framework-specific tools
• Webhook Support: Real-time notifications for AI system events
• Flexible Configuration: Adapt to any AI workflow requirements

Core AI Integration Features

1. Dynamic Response Management

Update API responses in real-time without restarting servers:

# Update responses based on AI system state
await mockloop.manage_mock_data(
    server_url="http://localhost:8000",
    operation="update_response",
    endpoint_path="/ai/model/status",
    response_data={
        "status": "training",
        "progress": 0.75,
        "eta_minutes": 15
    }
)

2. Scenario-Based Testing

Create comprehensive test scenarios for different AI system states:

# Create scenarios for different AI model states
scenarios = {
    "model_training": {
        "/ai/model/status": {"status": "training", "progress": 0.5},
        "/ai/predictions": {"error": "Model not ready", "status": 503}
    },
    "model_ready": {
        "/ai/model/status": {"status": "ready", "accuracy": 0.95},
        "/ai/predictions": {"predictions": [...], "confidence": 0.87}
    },
    "model_error": {
        "/ai/model/status": {"status": "error", "error": "Training failed"},
        "/ai/predictions": {"error": "Service unavailable", "status": 500}
    }
}

3. Performance Monitoring

Track AI system interactions with detailed analytics:

# Analyze AI system API usage
logs = await mockloop.query_mock_logs(
    server_url="http://localhost:8000",
    analyze=True,
    path_pattern="/ai/.*"
)

# Get insights on AI system behavior
print(f"AI API calls: {logs['analysis']['total_requests']}")
print(f"Average response time: {logs['analysis']['avg_response_time_ms']}ms")
print(f"Error rate: {logs['analysis']['error_rate_percent']}%")

4. Real-Time Adaptation

Adapt mock responses based on AI system behavior:

# Monitor AI system requests and adapt responses
def adapt_responses(request_logs):
    if request_logs['error_rate'] > 0.1:
        # Switch to error recovery scenario
        mockloop.manage_mock_data(
            operation="switch_scenario",
            scenario_name="error_recovery"
        )
    elif request_logs['avg_response_time'] > 1000:
        # Switch to high-performance scenario
        mockloop.manage_mock_data(
            operation="switch_scenario",
            scenario_name="fast_responses"
        )

AI Development Workflows

Workflow 1: AI Model Development

1. Setup Mock Data Sources

   # Generate mocks for training data APIs
   await mockloop.generate_mock_api(
       spec_url_or_path="./training-data-api.yaml",
       output_dir_name="training_data_mock"
   )
   

2. Create Training Scenarios

   # Different data quality scenarios
   scenarios = {
       "high_quality_data": {...},
       "noisy_data": {...},
       "missing_data": {...},
       "corrupted_data": {...}
   }
   

3. Train and Test Models

   for scenario in scenarios:
       # Switch to scenario
       await mockloop.manage_mock_data(
           operation="switch_scenario",
           scenario_name=scenario
       )
   
       # Train model with scenario data
       model = train_model(data_source="http://localhost:8000")
   
       # Evaluate performance
       metrics = evaluate_model(model)
   
       # Log results
       print(f"Scenario {scenario}: Accuracy = {metrics.accuracy}")
   

Workflow 2: AI Agent Testing

1. Setup Multi-Service Environment

   # Generate mocks for different services
   services = [
       {"spec": "./user-service.yaml", "port": 8001},
       {"spec": "./payment-service.yaml", "port": 8002},
       {"spec": "./inventory-service.yaml", "port": 8003}
   ]
   
   for service in services:
       await mockloop.generate_mock_api(
           spec_url_or_path=service["spec"],
           output_dir_name=f"service_{service['port']}"
       )
   

2. Create Agent Test Scenarios

   # Test different business scenarios
   scenarios = {
       "happy_path": {
           # All services working normally
           "user_service": {"status": "healthy"},
           "payment_service": {"status": "healthy"},
           "inventory_service": {"status": "healthy"}
       },
       "payment_failure": {
           # Payment service down
           "user_service": {"status": "healthy"},
           "payment_service": {"status": "error", "error": "Service unavailable"},
           "inventory_service": {"status": "healthy"}
       }
   }
   

3. Run Agent Tests

   for scenario_name, scenario_config in scenarios.items():
       # Configure all services for scenario
       for service, config in scenario_config.items():
           await mockloop.manage_mock_data(
               server_url=f"http://localhost:{service_ports[service]}",
               operation="update_response",
               endpoint_path="/health",
               response_data=config
           )
   
       # Run agent with scenario
       result = run_agent_scenario(scenario_name)
   
       # Analyze agent behavior
       logs = await mockloop.query_mock_logs(
           server_url="http://localhost:8001",
           analyze=True
       )
   

Workflow 3: RAG System Testing

1. Mock Knowledge Base APIs

   # Generate mock for document retrieval API
   await mockloop.generate_mock_api(
       spec_url_or_path="./knowledge-base-api.yaml",
       output_dir_name="knowledge_base_mock"
   )
   

2. Create Document Scenarios

   # Different document availability scenarios
   document_scenarios = {
       "comprehensive_docs": {
           "/search": {"documents": [...], "total": 100},
           "/document/{id}": {"content": "...", "metadata": {...}}
       },
       "limited_docs": {
           "/search": {"documents": [...], "total": 5},
           "/document/{id}": {"error": "Document not found", "status": 404}
       },
       "slow_retrieval": {
           "/search": {"documents": [...], "delay_ms": 2000},
           "/document/{id}": {"content": "...", "delay_ms": 1000}
       }
   }
   

3. Test RAG Performance

   for scenario in document_scenarios:
       # Configure knowledge base mock
       await mockloop.manage_mock_data(
           operation="switch_scenario",
           scenario_name=scenario
       )
   
       # Test RAG system
       questions = ["What is...?", "How do I...?", "Why does...?"]
       for question in questions:
           answer = rag_system.ask(question)
           quality = evaluate_answer_quality(question, answer)
   
           print(f"Scenario {scenario}, Question: {question}")
           print(f"Answer quality: {quality}")
   

Integration Patterns

Pattern 1: State-Driven Mocks

Synchronize mock responses with AI system state:

class StateDrivenMock:
    def __init__(self, mockloop_client, server_url):
        self.mockloop = mockloop_client
        self.server_url = server_url
        self.current_state = "initial"

    async def update_state(self, new_state, context=None):
        """Update mock responses based on AI system state"""
        self.current_state = new_state

        # Define state-specific responses
        state_responses = {
            "training": {
                "/model/status": {"status": "training", "progress": context.get("progress", 0)},
                "/predictions": {"error": "Model not ready", "status": 503}
            },
            "ready": {
                "/model/status": {"status": "ready", "accuracy": context.get("accuracy", 0.9)},
                "/predictions": {"predictions": context.get("predictions", [])}
            }
        }

        # Update all endpoints for the new state
        for endpoint, response in state_responses[new_state].items():
            await self.mockloop.manage_mock_data(
                server_url=self.server_url,
                operation="update_response",
                endpoint_path=endpoint,
                response_data=response
            )

Pattern 2: Behavior-Driven Testing

Test AI system behavior under different conditions:

class BehaviorDrivenTesting:
    def __init__(self, mockloop_client):
        self.mockloop = mockloop_client
        self.test_scenarios = {}

    async def define_behavior(self, behavior_name, conditions):
        """Define a behavior scenario"""
        self.test_scenarios[behavior_name] = conditions

        await self.mockloop.manage_mock_data(
            operation="create_scenario",
            scenario_name=behavior_name,
            scenario_config=conditions
        )

    async def test_behavior(self, behavior_name, ai_system):
        """Test AI system under specific behavior conditions"""
        # Switch to behavior scenario
        await self.mockloop.manage_mock_data(
            operation="switch_scenario",
            scenario_name=behavior_name
        )

        # Run AI system
        result = await ai_system.execute()

        # Analyze behavior
        logs = await self.mockloop.query_mock_logs(
            server_url=ai_system.api_endpoint,
            analyze=True
        )

        return {
            "behavior": behavior_name,
            "result": result,
            "api_interactions": logs["analysis"],
            "success": self.evaluate_behavior(result, behavior_name)
        }

Pattern 3: Adaptive Response Generation

Generate responses based on AI system requests:

class AdaptiveResponseGenerator:
    def __init__(self, mockloop_client):
        self.mockloop = mockloop_client
        self.request_history = []

    async def monitor_and_adapt(self, server_url):
        """Monitor requests and adapt responses"""
        while True:
            # Get recent logs
            logs = await self.mockloop.query_mock_logs(
                server_url=server_url,
                limit=10,
                analyze=False
            )

            # Analyze request patterns
            patterns = self.analyze_patterns(logs["logs"])

            # Adapt responses based on patterns
            if patterns["high_error_rate"]:
                await self.switch_to_recovery_mode(server_url)
            elif patterns["high_load"]:
                await self.optimize_for_performance(server_url)

            await asyncio.sleep(30)  # Check every 30 seconds

    async def switch_to_recovery_mode(self, server_url):
        """Switch to error recovery responses"""
        await self.mockloop.manage_mock_data(
            server_url=server_url,
            operation="switch_scenario",
            scenario_name="error_recovery"
        )

    async def optimize_for_performance(self, server_url):
        """Switch to high-performance responses"""
        await self.mockloop.manage_mock_data(
            server_url=server_url,
            operation="switch_scenario",
            scenario_name="fast_responses"
        )

Best Practices for AI Integration

1. Design for Testability

• Modular APIs: Design AI systems with clear API boundaries
• Configurable Endpoints: Make API endpoints configurable
• Graceful Degradation: Handle API failures gracefully
• Comprehensive Logging: Log all AI system decisions and API interactions

2. Create Realistic Scenarios

• Real-World Data: Use realistic data in mock responses
• Error Conditions: Include various error scenarios
• Performance Variations: Simulate different response times
• Edge Cases: Test boundary conditions and unusual inputs

3. Monitor and Analyze

• Continuous Monitoring: Track AI system behavior continuously
• Performance Metrics: Monitor response times and error rates
• Behavior Analysis: Analyze AI decision patterns
• Feedback Loops: Use insights to improve AI systems

4. Automate Testing

• CI/CD Integration: Include AI testing in deployment pipelines
• Regression Testing: Test AI systems against known scenarios
• Performance Benchmarks: Establish and monitor performance baselines
• Automated Scenarios: Create self-updating test scenarios

Getting Started

Ready to integrate MockLoop MCP with your AI framework? Choose your framework:

• LangGraph Integration: Build state-driven AI workflows
• CrewAI Integration: Test multi-agent systems
• LangChain Integration: Enhance chain testing
• Custom AI Workflows: Create custom integrations

Examples Repository

Find complete examples and templates in our examples repository:

• AI Model Training: Mock data sources for model training
• Multi-Agent Systems: Complex agent interaction scenarios
• RAG Applications: Knowledge base and retrieval testing
• Chatbot Testing: Conversation flow and response testing
• ML Pipeline Testing: End-to-end pipeline validation

Community and Support

• GitHub Discussions: Share AI integration patterns
• Example Contributions: Contribute AI workflow examples
• Framework Requests: Request support for new AI frameworks
• Best Practices: Share and learn AI testing best practices

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MockLoop MCP transforms AI development by providing realistic, controllable, and observable API environments. Start with your preferred framework and discover how mock servers can accelerate your AI development workflow!