MockLoop MCP

What is MockLoop MCP about?

MockLoop MCP provides a platform for creating sophisticated mock APIs directly from OpenAPI or Swagger definitions. It combines AI‑powered scenario generation, stateful context management, dual‑port mock/admin separation, and extensive analytics to support end‑to‑end API testing, performance monitoring, and security validation.

How to use MockLoop MCP?

1. Install the package

   pip install mockloop-mcp
   

2. Generate a mock server

   mockloop-mcp generate_mock_api \
       --spec-url-or-path path_or_url_to_openapi.yaml \
       --output-dir-name my_mock_api \
       --auth-enabled true \
       --admin-ui-enabled true
   

3. Run the generated mock (Docker Compose is recommended)

   cd my_mock_api
   docker-compose up --build
   

   Mocked API: http://localhost:8000 Admin UI: http://localhost:8001
4. Interact via MCP client (e.g., Cline, Claude Desktop) by adding the server configuration shown in the README.
5. Leverage AI tools – use built‑in MCP prompts such as analyze_openapi_for_testing, generate_scenario_config, run_load_test, etc., to create, execute, and analyze test scenarios.

Key Features of MockLoop MCP

• AI‑driven test generation – five specialized MCP prompts automatically create functional, load, error, and security scenarios.
• Dual‑port architecture – separate ports for the mock API (default 8000) and admin UI (default 8001) eliminate /admin path conflicts and improve security.
• Comprehensive audit logging – request/response trails, regulatory compliance tracking, and performance metrics.
• Stateful context management – test session, workflow, agent, and global contexts enable multi‑step, data‑driven testing.
• MCP proxy capability – switch between mock, live proxy, or hybrid modes at runtime with rule‑based routing and authentication support.
• Extensible AI integration – ready‑made examples for LangGraph, CrewAI, and LangChain agents.
• SchemaPin security – cryptographic schema verification protects against tampered tool definitions.
• Enterprise‑grade analytics – AI‑powered insights, traffic pattern detection, error categorization, and trend analysis.
• Scalable deployment – Docker, Kubernetes, horizontal scaling, caching, and load‑balancing support.

Use Cases of MockLoop MCP

• Early development – generate mock endpoints instantly to decouple front‑end work from back‑end implementation.
• CI/CD testing – embed generate_mock_api and AI scenario execution in pipelines for regression and contract testing.
• Performance benchmarking – run load tests with AI‑optimized scenarios and collect P95/P99 metrics.
• Security assessment – automatically generate vulnerability scenarios and compare mock vs. live responses.
• Hybrid migration – gradually migrate from mocked services to live APIs using the proxy/hybrid modes.
• Stateful e‑commerce flows – maintain session context across registration, cart, checkout, and fulfillment steps.
• Regulatory compliance – audit logs satisfy GDPR, SOX, HIPAA reporting requirements.

FAQ from MockLoop MCP

Q: Do I need Docker to run the mock server? A: Docker Compose is the recommended way because it automatically sets up the dual‑port configuration, but you can also run the server directly with Uvicorn (uvicorn main:app --port 8000).

Q: How does the AI generate test scenarios? A: The MCP prompts analyze the OpenAPI spec, infer typical usage patterns, and produce structured scenario configurations that the built‑in tools can deploy and execute.

Q: Can I customize the ports? A: Yes. The generate_mock_api command accepts --business-port and --admin-port arguments to override the defaults.

Q: What is the difference between mock, proxy, and hybrid modes? A: Mock serves only generated responses, Proxy forwards every request to the live API, and Hybrid routes requests based on user‑defined rules (e.g., critical endpoints go to live, dev endpoints stay mocked).

Q: Is SchemaPin required? A: No. It is enabled optionally to verify tool schemas. In production you can set policy_mode: "enforce" to block unverified tools.

Q: How do I integrate with LangChain or CrewAI? A: The README includes ready‑made code snippets that wrap MockLoop MCP functions as LangChain tools or CrewAI agents, allowing you to orchestrate AI‑native testing workflows.

Q: Where are logs stored? A: Logs are persisted in an SQLite database by default and exposed via the admin UI (/api/logs/...). They can be queried with the query_mock_logs tool for AI analysis.

NOTE: We have fully implemented SchemaPin to help combat questionable copies of this project on Github and elsewhere. Be sure validate you are using releases from this repo and can use SchemaPin to validate our tool schemas: https://mockloop.com/.well-known/schemapin.json

MockLoop MCP - AI-Native Testing Platform

The world's first AI-native API testing platform powered by the Model Context Protocol (MCP). MockLoop MCP revolutionizes API testing with comprehensive AI-driven scenario generation, automated test execution, and intelligent analysis capabilities.

🚀 Revolutionary Capabilities: 5 AI Prompts • 15 Scenario Resources • 16 Testing Tools • 10 Context Tools • 4 Core Tools • Complete MCP Integration

📚 Documentation: https://docs.mockloop.com
📦 PyPI Package: https://pypi.org/project/mockloop-mcp/
🐙 GitHub Repository: https://github.com/mockloop/mockloop-mcp

🌟 What Makes MockLoop MCP Revolutionary?

MockLoop MCP represents a paradigm shift in API testing, introducing the world's first AI-native testing architecture that combines:

• 🤖 AI-Driven Test Generation: 5 specialized MCP prompts for intelligent scenario creation
• 📦 Community Scenario Packs: 15 curated testing resources with community architecture
• ⚡ Automated Test Execution: 30 comprehensive MCP tools for complete testing workflows (16 testing + 10 context + 4 core)
• 🔄 Stateful Testing: Advanced context management with GlobalContext and AgentContext
• 📊 Enterprise Compliance: Complete audit logging and regulatory compliance tracking
• 🏗️ Dual-Port Architecture: Eliminates /admin path conflicts with separate mocked API and admin ports

🎯 Core AI-Native Architecture

MCP Audit Logging

Enterprise-grade compliance and regulatory tracking

• Complete request/response audit trails
• Regulatory compliance monitoring
• Performance metrics and analytics
• Security event logging

MCP Prompts (5 AI-Driven Capabilities)

Intelligent scenario generation powered by AI

• analyze_openapi_for_testing - Comprehensive API analysis for testing strategies
• generate_scenario_config - Dynamic test scenario configuration
• optimize_scenario_for_load - Load testing optimization
• generate_error_scenarios - Error condition simulation
• generate_security_test_scenarios - Security vulnerability testing

MCP Resources (15 Scenario Packs)

Community-driven testing scenarios with advanced architecture

• Load Testing Scenarios: High-volume traffic simulation
• Error Simulation Packs: Comprehensive error condition testing
• Security Testing Suites: Vulnerability assessment scenarios
• Performance Benchmarks: Standardized performance testing
• Integration Test Packs: Cross-service testing scenarios
• Community Architecture: Collaborative scenario sharing and validation

MCP Tools (16 Automated Testing Tools)

Complete automated test execution capabilities

Scenario Management (4 tools)

• validate_scenario_config - Scenario validation and verification
• deploy_scenario - Automated scenario deployment
• switch_scenario - Dynamic scenario switching
• list_active_scenarios - Active scenario monitoring

Test Execution (4 tools)

• execute_test_plan - Comprehensive test plan execution
• run_test_iteration - Individual test iteration management
• run_load_test - Load testing execution
• run_security_test - Security testing automation

Analysis & Reporting (4 tools)

• analyze_test_results - Intelligent test result analysis
• generate_test_report - Comprehensive reporting
• compare_test_runs - Test run comparison and trends
• get_performance_metrics - Performance metrics collection

Workflow Management (4 tools)

• create_test_session - Test session initialization
• end_test_session - Session cleanup and finalization
• schedule_test_suite - Automated test scheduling
• monitor_test_progress - Real-time progress monitoring

MCP Context Management (10 Stateful Workflow Tools)

Advanced state management for complex testing workflows

Context Creation & Management

• create_test_session_context - Test session state management
• create_workflow_context - Complex workflow orchestration
• create_agent_context - AI agent state management

Data Management

• get_context_data - Context data retrieval
• update_context_data - Dynamic context updates
• list_contexts_by_type - Context discovery and listing

Snapshot & Recovery

• create_context_snapshot - State snapshot creation
• restore_context_snapshot - State rollback capabilities

Global Context

• get_global_context_data - Cross-session data sharing
• update_global_context_data - Global state management

🚀 Quick Start

Get started with the world's most advanced AI-native testing platform:

# 1. Install MockLoop MCP
pip install mockloop-mcp

# 2. Verify installation
mockloop-mcp --version

# 3. Configure with your MCP client (Cline, Claude Desktop, etc.)
# See configuration examples below

📋 Prerequisites

• Python 3.10+
• Pip package manager
• Docker and Docker Compose (for containerized mock servers)
• An MCP-compatible client (Cline, Claude Desktop, etc.)

🔧 Installation

Option 1: Install from PyPI (Recommended)

# Install the latest stable version
pip install mockloop-mcp

# Or install with optional dependencies
pip install mockloop-mcp[dev]   # Development tools
pip install mockloop-mcp[docs]  # Documentation tools
pip install mockloop-mcp[all]   # All optional dependencies

# Verify installation
mockloop-mcp --version

Option 2: Development Installation

# Clone the repository
git clone https://github.com/mockloop/mockloop-mcp.git
cd mockloop-mcp

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

⚙️ Configuration

MCP Client Configuration

Cline (VS Code Extension)

Add to your Cline MCP settings file:

{
  "mcpServers": {
    "MockLoopLocal": {
      "autoApprove": [],
      "disabled": false,
      "timeout": 60,
      "command": "mockloop-mcp",
      "args": [],
      "transportType": "stdio"
    }
  }
}

Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "mockloop": {
      "command": "mockloop-mcp",
      "args": []
    }
  }
}

Virtual Environment Installations

For virtual environment installations, use the full Python path:

{
  "mcpServers": {
    "MockLoopLocal": {
      "command": "/path/to/your/venv/bin/python",
      "args": ["-m", "mockloop_mcp"],
      "transportType": "stdio"
    }
  }
}

🛠️ Available MCP Tools

Core Mock Generation

generate_mock_api

Generate sophisticated FastAPI mock servers with dual-port architecture.

Parameters:

• spec_url_or_path (string, required): API specification URL or local file path
• output_dir_name (string, optional): Output directory name
• auth_enabled (boolean, optional): Enable authentication middleware (default: true)
• webhooks_enabled (boolean, optional): Enable webhook support (default: true)
• admin_ui_enabled (boolean, optional): Enable admin UI (default: true)
• storage_enabled (boolean, optional): Enable storage functionality (default: true)

Revolutionary Dual-Port Architecture:

• Mocked API Port: Serves your API endpoints (default: 8000)
• Admin UI Port: Separate admin interface (default: 8001)
• Conflict Resolution: Eliminates /admin path conflicts in OpenAPI specs
• Enhanced Security: Port-based access control and isolation

Advanced Analytics

query_mock_logs

Query and analyze request logs with AI-powered insights.

Parameters:

• server_url (string, required): Mock server URL
• limit (integer, optional): Maximum logs to return (default: 100)
• offset (integer, optional): Pagination offset (default: 0)
• method (string, optional): Filter by HTTP method
• path_pattern (string, optional): Regex pattern for path filtering
• time_from (string, optional): Start time filter (ISO format)
• time_to (string, optional): End time filter (ISO format)
• include_admin (boolean, optional): Include admin requests (default: false)
• analyze (boolean, optional): Perform AI analysis (default: true)

AI-Powered Analysis:

• Performance metrics (P95/P99 response times)
• Error rate analysis and categorization
• Traffic pattern detection
• Automated debugging recommendations
• Session correlation and tracking

discover_mock_servers

Intelligent server discovery with dual-port architecture support.

Parameters:

• ports (array, optional): Ports to scan (default: common ports)
• check_health (boolean, optional): Perform health checks (default: true)
• include_generated (boolean, optional): Include generated mocks (default: true)

Advanced Discovery:

• Automatic architecture detection (single-port vs dual-port)
• Health status monitoring
• Server correlation and matching
• Port usage analysis

manage_mock_data

Dynamic response management without server restart.

Parameters:

• server_url (string, required): Mock server URL
• operation (string, required): Operation type ("update_response", "create_scenario", "switch_scenario", "list_scenarios")
• endpoint_path (string, optional): API endpoint path
• response_data (object, optional): New response data
• scenario_name (string, optional): Scenario name
• scenario_config (object, optional): Scenario configuration

Dynamic Capabilities:

• Real-time response updates
• Scenario-based testing
• Runtime configuration management
• Zero-downtime modifications

🌐 MCP Proxy Functionality

MockLoop MCP includes revolutionary proxy capabilities that enable seamless switching between mock and live API environments. This powerful feature transforms your testing workflow by providing:

Core Proxy Capabilities

• 🔄 Seamless Mode Switching: Transition between mock, proxy, and hybrid modes without code changes
• 🎯 Intelligent Routing: Smart request routing based on configurable rules and conditions
• 🔐 Universal Authentication: Support for API Key, Bearer Token, Basic Auth, and OAuth2
• 📊 Response Comparison: Automated comparison between mock and live API responses
• ⚡ Zero-Downtime Switching: Change modes dynamically without service interruption

Operational Modes

Mock Mode (MOCK)

• All requests handled by generated mock responses
• Predictable, consistent testing environment
• Ideal for early development and isolated testing
• No external dependencies or network calls

Proxy Mode (PROXY)

• All requests forwarded to live API endpoints
• Real-time data and authentic responses
• Full integration testing capabilities
• Network-dependent operation with live credentials

Hybrid Mode (HYBRID)

• Intelligent routing between mock and proxy based on rules
• Conditional switching based on request patterns, headers, or parameters
• Gradual migration from mock to live environments
• A/B testing and selective endpoint proxying

Quick Start Example

from mockloop_mcp.mcp_tools import create_mcp_plugin

# Create a proxy-enabled plugin
plugin_result = await create_mcp_plugin(
    spec_url_or_path="https://api.example.com/openapi.json",
    mode="hybrid",  # Start with hybrid mode
    plugin_name="example_api",
    target_url="https://api.example.com",
    auth_config={
        "auth_type": "bearer_token",
        "credentials": {"token": "your-token"}
    },
    routing_rules=[
        {
            "pattern": "/api/critical/*",
            "mode": "proxy",  # Critical endpoints use live API
            "priority": 10
        },
        {
            "pattern": "/api/dev/*",
            "mode": "mock",   # Development endpoints use mocks
            "priority": 5
        }
    ]
)

Advanced Features

• 🔍 Response Validation: Compare mock vs live responses for consistency
• 📈 Performance Monitoring: Track response times and throughput across modes
• 🛡️ Error Handling: Graceful fallback mechanisms and retry policies
• 🎛️ Dynamic Configuration: Runtime mode switching and rule updates
• 📋 Audit Logging: Complete request/response tracking across all modes

Authentication Support

The proxy system supports comprehensive authentication schemes:

• API Key: Header, query parameter, or cookie-based authentication
• Bearer Token: OAuth2 and JWT token support
• Basic Auth: Username/password combinations
• OAuth2: Full OAuth2 flow with token refresh
• Custom: Extensible authentication handlers for proprietary schemes

Use Cases

• Development Workflow: Start with mocks, gradually introduce live APIs
• Integration Testing: Validate against real services while maintaining test isolation
• Performance Testing: Compare mock vs live API performance characteristics
• Staging Validation: Ensure mock responses match production API behavior
• Hybrid Deployments: Route critical operations to live APIs, others to mocks

📚 Complete Guide: For detailed configuration, examples, and best practices, see the MCP Proxy Guide.

🤖 AI Framework Integration

MockLoop MCP provides native integration with popular AI frameworks:

LangGraph Integration

from langgraph.graph import StateGraph, END
from mockloop_mcp import MockLoopClient

# Initialize MockLoop client
mockloop = MockLoopClient()

def setup_ai_testing(state):
    """AI-driven test setup"""
    # Generate mock API with AI analysis
    result = mockloop.generate_mock_api(
        spec_url_or_path="https://api.example.com/openapi.json",
        output_dir_name="ai_test_environment"
    )
    
    # Use AI prompts for scenario generation
    scenarios = mockloop.analyze_openapi_for_testing(
        api_spec=state["api_spec"],
        analysis_depth="comprehensive",
        include_security_tests=True
    )
    
    state["mock_server_url"] = "http://localhost:8000"
    state["test_scenarios"] = scenarios
    return state

def execute_ai_tests(state):
    """Execute AI-generated test scenarios"""
    # Deploy AI-generated scenarios
    for scenario in state["test_scenarios"]:
        mockloop.deploy_scenario(
            server_url=state["mock_server_url"],
            scenario_config=scenario
        )
        
        # Execute load tests with AI optimization
        results = mockloop.run_load_test(
            server_url=state["mock_server_url"],
            scenario_name=scenario["name"],
            duration=300,
            concurrent_users=100
        )
        
        # AI-powered result analysis
        analysis = mockloop.analyze_test_results(
            test_results=results,
            include_recommendations=True
        )
        
        state["test_results"].append(analysis)
    
    return state

# Build AI-native testing workflow
workflow = StateGraph(dict)
workflow.add_node("setup_ai_testing", setup_ai_testing)
workflow.add_node("execute_ai_tests", execute_ai_tests)
workflow.set_entry_point("setup_ai_testing")
workflow.add_edge("setup_ai_testing", "execute_ai_tests")
workflow.add_edge("execute_ai_tests", END)

app = workflow.compile()

CrewAI Multi-Agent Testing

from crewai import Agent, Task, Crew
from mockloop_mcp import MockLoopClient

# Initialize MockLoop client
mockloop = MockLoopClient()

# AI Testing Specialist Agent
api_testing_agent = Agent(
    role='AI API Testing Specialist',
    goal='Generate and execute comprehensive AI-driven API tests',
    backstory='Expert in AI-native testing with MockLoop MCP integration',
    tools=[
        mockloop.generate_mock_api,
        mockloop.analyze_openapi_for_testing,
        mockloop.generate_scenario_config
    ]
)

# Performance Analysis Agent
performance_agent = Agent(
    role='AI Performance Analyst',
    goal='Analyze API performance with AI-powered insights',
    backstory='Specialist in AI-driven performance analysis and optimization',
    tools=[
        mockloop.run_load_test,
        mockloop.get_performance_metrics,
        mockloop.analyze_test_results
    ]
)

# Security Testing Agent
security_agent = Agent(
    role='AI Security Testing Expert',
    goal='Conduct AI-driven security testing and vulnerability assessment',
    backstory='Expert in AI-powered security testing methodologies',
    tools=[
        mockloop.generate_security_test_scenarios,
        mockloop.run_security_test,
        mockloop.compare_test_runs
    ]
)

# Define AI-driven tasks
ai_setup_task = Task(
    description='Generate AI-native mock API with comprehensive testing scenarios',
    agent=api_testing_agent,
    expected_output='Mock server with AI-generated test scenarios deployed'
)

performance_task = Task(
    description='Execute AI-optimized performance testing and analysis',
    agent=performance_agent,
    expected_output='Comprehensive performance analysis with AI recommendations'
)

security_task = Task(
    description='Conduct AI-driven security testing and vulnerability assessment',
    agent=security_agent,
    expected_output='Security test results with AI-powered threat analysis'
)

# Create AI testing crew
ai_testing_crew = Crew(
    agents=[api_testing_agent, performance_agent, security_agent],
    tasks=[ai_setup_task, performance_task, security_task],
    verbose=True
)

# Execute AI-native testing workflow
results = ai_testing_crew.kickoff()

LangChain AI Testing Tools

from langchain.agents import Tool, AgentExecutor, create_react_agent
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from mockloop_mcp import MockLoopClient

# Initialize MockLoop client
mockloop = MockLoopClient()

# AI-Native Testing Tools
def ai_generate_mock_api(spec_path: str) -> str:
    """Generate AI-enhanced mock API with intelligent scenarios"""
    # Generate mock API
    result = mockloop.generate_mock_api(spec_url_or_path=spec_path)
    
    # Use AI to analyze and enhance
    analysis = mockloop.analyze_openapi_for_testing(
        api_spec=spec_path,
        analysis_depth="comprehensive",
        include_security_tests=True
    )
    
    return f"AI-enhanced mock API generated: {result}\nAI Analysis: {analysis['summary']}"

def ai_execute_testing_workflow(server_url: str) -> str:
    """Execute comprehensive AI-driven testing workflow"""
    # Create test session context
    session = mockloop.create_test_session_context(
        session_name="ai_testing_session",
        configuration={"ai_enhanced": True}
    )
    
    # Generate and deploy AI scenarios
    scenarios = mockloop.generate_scenario_config(
        api_spec=server_url,
        scenario_types=["load", "error", "security"],
        ai_optimization=True
    )
    
    results = []
    for scenario in scenarios:
        # Deploy scenario
        mockloop.deploy_scenario(
            server_url=server_url,
            scenario_config=scenario
        )
        
        # Execute tests with AI monitoring
        test_result = mockloop.execute_test_plan(
            server_url=server_url,
            test_plan=scenario["test_plan"],
            ai_monitoring=True
        )
        
        results.append(test_result)
    
    # AI-powered analysis
    analysis = mockloop.analyze_test_results(
        test_results=results,
        include_recommendations=True,
        ai_insights=True
    )
    
    return f"AI testing workflow completed: {analysis['summary']}"

# Create LangChain tools
ai_testing_tools = [
    Tool(
        name="AIGenerateMockAPI",
        func=ai_generate_mock_api,
        description="Generate AI-enhanced mock API with intelligent testing scenarios"
    ),
    Tool(
        name="AIExecuteTestingWorkflow",
        func=ai_execute_testing_workflow,
        description="Execute comprehensive AI-driven testing workflow with intelligent analysis"
    )
]

# Create AI testing agent
llm = ChatOpenAI(temperature=0)
ai_testing_prompt = PromptTemplate.from_template("""
You are an AI-native testing assistant powered by MockLoop MCP.
You have access to revolutionary AI-driven testing capabilities including:
- AI-powered scenario generation
- Intelligent test execution
- Advanced performance analysis
- Security vulnerability assessment
- Stateful workflow management

Tools available: {tools}
Tool names: {tool_names}

Question: {input}
{agent_scratchpad}
""")

agent = create_react_agent(llm, ai_testing_tools, ai_testing_prompt)
agent_executor = AgentExecutor(agent=agent, tools=ai_testing_tools, verbose=True)

# Execute AI-native testing
response = agent_executor.invoke({
    "input": "Generate a comprehensive AI-driven testing environment for a REST API and execute full testing workflow"
})

🏗️ Dual-Port Architecture

MockLoop MCP introduces a revolutionary dual-port architecture that eliminates common conflicts and enhances security:

Architecture Benefits

• 🔒 Enhanced Security: Complete separation of mocked API and admin functionality
• ⚡ Zero Conflicts: Eliminates /admin path conflicts in OpenAPI specifications
• 📊 Clean Analytics: Admin calls don't appear in mocked API metrics
• 🔄 Independent Scaling: Scale mocked API and admin services separately
• 🛡️ Port-Based Access Control: Enhanced security through network isolation

Port Configuration

# Generate mock with dual-port architecture
result = mockloop.generate_mock_api(
    spec_url_or_path="https://api.example.com/openapi.json",
    business_port=8000,  # Mocked API port
    admin_port=8001,     # Admin UI port
    admin_ui_enabled=True
)

Access Points

• Mocked API: http://localhost:8000 - Your API endpoints
• Admin UI: http://localhost:8001 - Management interface
• API Documentation: http://localhost:8000/docs - Interactive Swagger UI
• Health Check: http://localhost:8000/health - Server status

📊 Enterprise Features

Compliance & Audit Logging

MockLoop MCP provides enterprise-grade compliance features:

• Complete Audit Trails: Every request/response logged with metadata
• Regulatory Compliance: GDPR, SOX, HIPAA compliance support
• Performance Metrics: P95/P99 response times, error rates
• Security Monitoring: Threat detection and analysis
• Session Tracking: Cross-request correlation and analysis

Advanced Analytics

• AI-Powered Insights: Intelligent analysis and recommendations
• Traffic Pattern Detection: Automated anomaly detection
• Performance Optimization: AI-driven performance recommendations
• Error Analysis: Intelligent error categorization and resolution
• Trend Analysis: Historical performance and usage trends

🔄 Stateful Testing Workflows

MockLoop MCP supports complex, stateful testing workflows through advanced context management:

Context Types

• Test Session Context: Maintain state across test executions
• Workflow Context: Complex multi-step testing orchestration
• Agent Context: AI agent state management and coordination
• Global Context: Cross-session data sharing and persistence

Example: Stateful E-commerce Testing

# Create test session context
session = mockloop.create_test_session_context(
    session_name="ecommerce_integration_test",
    configuration={
        "test_type": "integration",
        "environment": "staging",
        "ai_enhanced": True
    }
)

# Create workflow context for multi-step testing
workflow = mockloop.create_workflow_context(
    workflow_name="user_journey_test",
    parent_context=session["context_id"],
    steps=[
        "user_registration",
        "product_browsing",
        "cart_management",
        "checkout_process",
        "order_fulfillment"
    ]
)

# Execute stateful test workflow
for step in workflow["steps"]:
    # Update context with step data
    mockloop.update_context_data(
        context_id=workflow["context_id"],
        data={"current_step": step, "timestamp": datetime.now()}
    )
    
    # Execute step-specific tests
    test_result = mockloop.execute_test_plan(
        server_url="http://localhost:8000",
        test_plan=f"{step}_test_plan",
        context_id=workflow["context_id"]
    )
    
    # Create snapshot for rollback capability
    snapshot = mockloop.create_context_snapshot(
        context_id=workflow["context_id"],
        snapshot_name=f"{step}_completion"
    )

# Analyze complete workflow results
final_analysis = mockloop.analyze_test_results(
    test_results=workflow["results"],
    context_id=workflow["context_id"],
    include_recommendations=True
)

🚀 Running Generated Mock Servers

Using Docker Compose (Recommended)

# Navigate to generated mock directory
cd generated_mocks/your_api_mock

# Start with dual-port architecture
docker-compose up --build

# Access points:
# Mocked API: http://localhost:8000
# Admin UI: http://localhost:8001

Using Uvicorn Directly

# Install dependencies
pip install -r requirements_mock.txt

# Start the mock server
uvicorn main:app --reload --port 8000

Enhanced Features Access

• Admin UI: http://localhost:8001 - Enhanced management interface
• API Documentation: http://localhost:8000/docs - Interactive Swagger UI
• Health Check: http://localhost:8000/health - Server status and metrics
• Log Analytics: http://localhost:8001/api/logs/search - Advanced log querying
• Performance Metrics: http://localhost:8001/api/logs/analyze - AI-powered insights
• Scenario Management: http://localhost:8001/api/mock-data/scenarios - Dynamic testing

📈 Performance & Scalability

MockLoop MCP is designed for enterprise-scale performance:

Performance Metrics

• Response Times: P50, P95, P99 percentile tracking
• Throughput: Requests per second monitoring
• Error Rates: Comprehensive error analysis
• Resource Usage: Memory, CPU, and network monitoring
• Concurrency: Multi-user load testing support

Scalability Features

• Horizontal Scaling: Multi-instance deployment support
• Load Balancing: Built-in load balancing capabilities
• Caching: Intelligent response caching
• Database Optimization: Efficient SQLite and PostgreSQL support
• Container Orchestration: Kubernetes and Docker Swarm ready

🔒 Security Features

Built-in Security

• Authentication Middleware: Configurable auth mechanisms
• Rate Limiting: Prevent abuse and DoS attacks
• Input Validation: Comprehensive request validation
• Security Headers: CORS, CSP, and security headers
• Audit Logging: Complete security event logging

Security Testing

• Vulnerability Assessment: AI-powered security testing
• Penetration Testing: Automated security scenario generation
• Compliance Checking: Security standard compliance verification
• Threat Modeling: AI-driven threat analysis
• Security Reporting: Comprehensive security analytics

🔐 SchemaPin Integration - Cryptographic Schema Verification

MockLoop MCP now includes SchemaPin integration - the industry's first cryptographic schema verification system for MCP tools, preventing "MCP Rug Pull" attacks through ECDSA signature verification and Trust-On-First-Use (TOFU) key pinning.

Revolutionary Security Enhancement

SchemaPin integration transforms MockLoop MCP into the most secure MCP testing platform by providing:

• 🔐 Cryptographic Verification: ECDSA P-256 signatures ensure schema integrity
• 🔑 TOFU Key Pinning: Automatic key discovery and pinning for trusted domains
• 📋 Policy Enforcement: Configurable security policies (enforce/warn/log modes)
• 📊 Comprehensive Auditing: Complete verification logs for compliance
• 🔄 Graceful Fallback: Works with or without SchemaPin library
• 🏗️ Hybrid Architecture: Seamless integration with existing MockLoop systems

Quick Start Configuration

from mockloop_mcp.schemapin import SchemaPinConfig, SchemaVerificationInterceptor

# Basic configuration
config = SchemaPinConfig(
    enabled=True,
    policy_mode="warn",  # enforce, warn, or log
    auto_pin_keys=False,
    trusted_domains=["api.example.com"],
    interactive_mode=False
)

# Initialize verification
interceptor = SchemaVerificationInterceptor(config)

# Verify tool schema
result = await interceptor.verify_tool_schema(
    tool_name="database_query",
    schema=tool_schema,
    signature="base64_encoded_signature",
    domain="api.example.com"
)

if result.valid:
    print("✓ Schema verification successful")
else:
    print(f"✗ Verification failed: {result.error}")

Production Configuration

# Production-ready configuration
config = SchemaPinConfig(
    enabled=True,
    policy_mode="enforce",  # Block execution on verification failure
    auto_pin_keys=True,     # Auto-pin keys for trusted domains
    key_pin_storage_path="/secure/path/keys.db",
    discovery_timeout=60,
    cache_ttl=7200,
    trusted_domains=[
        "api.corp.com",
        "tools.internal.com"
    ],
    well_known_endpoints={
        "api.corp.com": "https://api.corp.com/.well-known/schemapin.json"
    },
    revocation_check=True,
    interactive_mode=False
)

Security Benefits

MCP Rug Pull Protection

SchemaPin prevents malicious actors from modifying tool schemas without detection:

• Cryptographic Signatures: Every tool schema is cryptographically signed
• Key Pinning: TOFU model prevents man-in-the-middle attacks
• Audit Trails: Complete verification logs for security analysis
• Policy Enforcement: Configurable responses to verification failures

Compliance & Governance

• Regulatory Compliance: Audit logs support GDPR, SOX, HIPAA requirements
• Enterprise Security: Integration with existing security frameworks
• Risk Management: Configurable security policies for different environments
• Threat Detection: Automated detection of schema tampering attempts

Integration Examples

Basic Tool Verification

# Verify a single tool
from mockloop_mcp.schemapin import SchemaVerificationInterceptor

interceptor = SchemaVerificationInterceptor(config)
result = await interceptor.verify_tool_schema(
    "api_call", tool_schema, signature, "api.example.com"
)

Batch Verification

# Verify multiple tools efficiently
from mockloop_mcp.schemapin import SchemaPinWorkflowManager

workflow = SchemaPinWorkflowManager(config)
results = await workflow.verify_tool_batch([
    {"name": "tool1", "schema": schema1, "signature": sig1, "domain": "api.com"},
    {"name": "tool2", "schema": schema2, "signature": sig2, "domain": "api.com"}
])

MCP Proxy Integration

# Integrate with MCP proxy for seamless security
class SecureMCPProxy:
    def __init__(self, config):
        self.interceptor = SchemaVerificationInterceptor(config)
    
    async def proxy_tool_request(self, tool_name, schema, signature, domain, data):
        # Verify schema before execution
        result = await self.interceptor.verify_tool_schema(
            tool_name, schema, signature, domain
        )
        
        if not result.valid:
            return {"error": "Schema verification failed"}
        
        # Execute tool with verified schema
        return await self.execute_tool(tool_name, data)

Policy Modes

Enforce Mode

config = SchemaPinConfig(policy_mode="enforce")
# Blocks execution on verification failure
# Recommended for production critical tools

Warn Mode

config = SchemaPinConfig(policy_mode="warn")
# Logs warnings but allows execution
# Recommended for gradual rollout

Log Mode

config = SchemaPinConfig(policy_mode="log")
# Logs events without blocking
# Recommended for monitoring and testing

Key Management

Trust-On-First-Use (TOFU)

# Automatic key discovery and pinning
key_manager = KeyPinningManager("keys.db")

# Pin key for trusted tool
success = key_manager.pin_key(
    tool_id="api.example.com/database_query",
    domain="api.example.com",
    public_key_pem=discovered_key,
    metadata={"developer": "Example Corp"}
)

# Check if key is pinned
if key_manager.is_key_pinned("api.example.com/database_query"):
    print("Key is pinned and trusted")

Key Discovery

SchemaPin automatically discovers public keys via .well-known endpoints:

https://api.example.com/.well-known/schemapin.json

Expected format:

{
  "public_key": "-----BEGIN PUBLIC KEY-----\n...\n-----END PUBLIC KEY-----",
  "algorithm": "ES256",
  "created_at": "2023-01-01T00:00:00Z"
}

Audit & Compliance

Comprehensive Logging

from mockloop_mcp.schemapin import SchemaPinAuditLogger

audit_logger = SchemaPinAuditLogger("audit.db")

# Verification events are automatically logged
stats = audit_logger.get_verification_stats()
print(f"Total verifications: {stats['total_verifications']}")
print(f"Success rate: {stats['successful_verifications'] / stats['total_verifications'] * 100:.1f}%")

Compliance Reporting

# Generate compliance reports
from mockloop_mcp.mcp_compliance import MCPComplianceReporter

reporter = MCPComplianceReporter("audit.db")
report = reporter.generate_schemapin_compliance_report()

print(f"Compliance score: {report['compliance_score']:.1f}%")
print(f"Verification coverage: {report['verification_statistics']['unique_tools']} tools")

Documentation & Examples

• 📚 Complete Integration Guide: docs/guides/schemapin-integration.md
• 🔧 Basic Usage Example: examples/schemapin/basic_usage.py
• ⚡ Advanced Patterns: examples/schemapin/advanced_usage.py
• 🏗️ Architecture Documentation: SchemaPin_MockLoop_Integration_Architecture.md
• 🧪 Test Coverage: 56 comprehensive tests (42 unit + 14 integration)

Migration for Existing Users

SchemaPin integration is completely backward compatible:

1. Opt-in Configuration: SchemaPin is disabled by default
2. No Breaking Changes: Existing tools continue to work unchanged
3. Gradual Rollout: Start with log mode, progress to warn, then enforce
4. Zero Downtime: Enable verification without service interruption

# Migration example: gradual rollout
# Phase 1: Monitoring (log mode)
config = SchemaPinConfig(enabled=True, policy_mode="log")

# Phase 2: Warnings (warn mode)
config = SchemaPinConfig(enabled=True, policy_mode="warn")

# Phase 3: Enforcement (enforce mode)
config = SchemaPinConfig(enabled=True, policy_mode="enforce")

Performance Impact

SchemaPin is designed for minimal performance impact:

• Verification Time: ~5-15ms per tool (cached results)
• Memory Usage: <10MB additional memory
• Network Overhead: Key discovery only on first use
• Database Size: ~1KB per pinned key

Use Cases

Development Teams

• Secure Development: Verify tool schemas during development
• Code Review: Ensure schema integrity in pull requests
• Testing: Validate tool behavior with verified schemas

Enterprise Security

• Threat Prevention: Block malicious schema modifications
• Compliance: Meet regulatory requirements with audit trails
• Risk Management: Configurable security policies
• Incident Response: Detailed logs for security analysis

DevOps & CI/CD

• Pipeline Security: Verify schemas in deployment pipelines
• Environment Promotion: Ensure schema consistency across environments
• Monitoring: Continuous verification monitoring
• Automation: Automated security policy enforcement

�️ Future Development

Upcoming Features 🚧

Enhanced AI Capabilities

• Advanced ML Models: Custom model training for API testing
• Predictive Analytics: AI-powered failure prediction
• Intelligent Test Generation: Self-improving test scenarios
• Natural Language Testing: Plain English test descriptions

Extended Protocol Support

• GraphQL Support: Native GraphQL API testing
• gRPC Integration: Protocol buffer testing support
• WebSocket Testing: Real-time communication testing
• Event-Driven Testing: Async and event-based API testing

Enterprise Integration

• CI/CD Integration: Native pipeline integration
• Monitoring Platforms: Datadog, New Relic, Prometheus integration
• Identity Providers: SSO and enterprise auth integration
• Compliance Frameworks: Extended regulatory compliance support

🤝 Contributing

We welcome contributions to MockLoop MCP! Please see our Contributing Guidelines for details.

Development Setup

# Fork and clone the repository
git clone https://github.com/your-username/mockloop-mcp.git
cd mockloop-mcp

# Create development environment
python3 -m venv .venv
source .venv/bin/activate

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Run quality checks
ruff check src/
bandit -r src/

Community

• GitHub Repository: mockloop/mockloop-mcp
• Issues & Bug Reports: GitHub Issues
• Feature Requests: GitHub Issues
• Documentation: docs.mockloop.com

📄 License

MockLoop MCP is licensed under the MIT License.

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