Quick Start
Get up and running with MockLoop MCP in just a few minutes! This guide will walk you through generating your first mock API server.
Prerequisites
Before starting, ensure you have:
- ✅ Installed MockLoop MCP from PyPI
- ✅ An MCP client configured (Cline or Claude Desktop)
- ✅ A sample OpenAPI specification (we'll provide one)
Step 1: Install MockLoop MCP
If you haven't already, install MockLoop MCP from PyPI:
# Create and activate virtual environment (recommended)
python3 -m venv mockloop-env
source mockloop-env/bin/activate # On Windows: mockloop-env\Scripts\activate
# Install MockLoop MCP
pip install mockloop-mcp
# Verify installation
mockloop-mcp --version
Step 2: Configure Your MCP Client
The MockLoop MCP server runs automatically when configured with your MCP client. No manual server startup is required.
Using Cline (VS Code Extension)
- Open VS Code with the Cline extension installed
- Configure MCP Settings by adding MockLoop to your Cline MCP settings file:
{
"mcpServers": {
"MockLoopLocal": {
"autoApprove": [],
"disabled": false,
"timeout": 60,
"command": "mockloop-mcp",
"args": [],
"transportType": "stdio"
}
}
}
Alternative for virtual environment:
{
"mcpServers": {
"MockLoopLocal": {
"autoApprove": [],
"disabled": false,
"timeout": 60,
"command": "/path/to/your/mockloop-env/bin/python",
"args": ["-m", "mockloop_mcp"],
"transportType": "stdio"
}
}
}
- Restart Cline to load the new configuration
Using Claude Desktop
Add the following to your Claude Desktop configuration file:
Alternative for virtual environment:
{
"mcpServers": {
"mockloop": {
"command": "/path/to/your/mockloop-env/bin/python",
"args": ["-m", "mockloop_mcp"]
}
}
}
Step 3: Generate Your First Mock Server
Now let's generate a mock server using the Petstore API as an example:
Using the MCP Tool
In your MCP client, use the generate_mock_api
tool:
Please generate a mock API server using the Petstore OpenAPI specification:
https://petstore3.swagger.io/api/v3/openapi.json
Expected Output
MockLoop MCP will:
- Download the OpenAPI specification
- Parse the API definition
- Generate a complete FastAPI mock server
- Create Docker configuration files
- Set up request/response logging
You'll see a new directory created: generated_mocks/petstore_api/
Step 4: Explore the Generated Files
Navigate to your generated mock server:
You should see:
├── main.py # FastAPI application
├── requirements_mock.txt # Dependencies
├── Dockerfile # Docker image configuration
├── docker-compose.yml # Docker Compose setup
├── logging_middleware.py # Request/response logging
├── templates/
│ └── admin.html # Admin UI
└── db/ # SQLite database directory
Step 5: Run Your Mock Server
Option A: Using Docker Compose (Recommended)
Option B: Using Python Directly
# Install dependencies
pip install -r requirements_mock.txt
# Run the server
uvicorn main:app --reload --port 8000
Step 6: Test Your Mock Server
Once running, your mock server will be available at http://localhost:8000
.
Access Points
- API Documentation:
http://localhost:8000/docs
- Admin UI:
http://localhost:8000/admin
- Health Check:
http://localhost:8000/health
- API Endpoints:
http://localhost:8000/pet
,http://localhost:8000/store/order
, etc.
Test API Endpoints
Try making some requests:
# Get all pets
curl http://localhost:8000/pet/findByStatus?status=available
# Get a specific pet
curl http://localhost:8000/pet/1
# Create a new pet (POST)
curl -X POST http://localhost:8000/pet \
-H "Content-Type: application/json" \
-d '{"name": "Fluffy", "status": "available"}'
Step 7: Explore Advanced Features
Admin UI
Visit http://localhost:8000/admin
to access the admin interface:
- Dashboard: Overview of server status and metrics
- Request Logs: View all incoming requests and responses
- Log Analytics: Performance metrics and insights
- Webhooks: Configure webhook endpoints
- API Documentation: Links to Swagger UI and ReDoc
Request Logging
All requests are automatically logged to a SQLite database. You can query logs using the MCP tools:
Performance Monitoring
MockLoop automatically tracks: - Response times (P95, P99 percentiles) - Error rates - Traffic patterns - Session correlation
Next Steps
Congratulations! You've successfully created and run your first mock server. Here's what to explore next:
Learn More Features
- Configuration: Customize your mock servers
- Advanced Features: Explore dynamic responses and scenarios
- AI Integration: Integrate with AI frameworks
Explore MCP Tools
query_mock_logs
: Analyze request patterns and performancediscover_mock_servers
: Find and manage running serversmanage_mock_data
: Update responses and create scenarios
Common Use Cases
- API Development: Mock dependencies while building your API
- Frontend Development: Create realistic backend responses
- Testing: Generate test scenarios and edge cases
- Documentation: Provide interactive API examples
Troubleshooting
Server Won't Start
Docker Issues
MCP Connection Issues
- Verify the MCP server is running
- Check file paths in your MCP client configuration
- Ensure Python virtual environment is activated
- Check for any error messages in the MCP client
Getting Help
If you encounter issues:
1. Check the Troubleshooting Guide
2. Review GitHub Issues
Example Specifications
Here are some popular OpenAPI specifications you can try:
- Petstore:
https://petstore3.swagger.io/api/v3/openapi.json
- JSONPlaceholder:
https://jsonplaceholder.typicode.com/
- GitHub API:
https://api.github.com/
- Stripe API:
https://raw.githubusercontent.com/stripe/openapi/master/openapi/spec3.json
Ready to dive deeper? Continue to the Configuration Guide to learn how to customize your mock servers!