{"id":29114055,"url":"https://github.com/mockloop/mockloop-mcp","last_synced_at":"2025-09-04T10:43:16.261Z","repository":{"id":296131592,"uuid":"992294109","full_name":"MockLoop/mockloop-mcp","owner":"MockLoop","description":"Intelligent Model Context Protocol (MCP) server for AI-assisted API development. Generate mock servers from OpenAPI specs with advanced logging, performance analytics, and server discovery. Optimized for AI development workflows with comprehensive testing insights and automated analysis.","archived":false,"fork":false,"pushed_at":"2025-07-27T01:22:04.000Z","size":4176,"stargazers_count":8,"open_issues_count":2,"forks_count":3,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-27T04:33:51.304Z","etag":null,"topics":["ai","api","feedback-loop","llm","mcp","mcp-server","mcp-servers","mock","mocking-server","mocking-utility","models","openapi","swagger","training","training-data"],"latest_commit_sha":null,"homepage":"https://mockloop.com","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MockLoop.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"docs/contributing/community-support.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":".github/SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-28T23:35:30.000Z","updated_at":"2025-07-27T01:22:07.000Z","dependencies_parsed_at":"2025-07-14T08:51:23.856Z","dependency_job_id":null,"html_url":"https://github.com/MockLoop/mockloop-mcp","commit_stats":null,"previous_names":["mockloop/mockloop-mcp"],"tags_count":11,"template":false,"template_full_name":null,"purl":"pkg:github/MockLoop/mockloop-mcp","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MockLoop%2Fmockloop-mcp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MockLoop%2Fmockloop-mcp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MockLoop%2Fmockloop-mcp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MockLoop%2Fmockloop-mcp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MockLoop","download_url":"https://codeload.github.com/MockLoop/mockloop-mcp/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MockLoop%2Fmockloop-mcp/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267320257,"owners_count":24068527,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-27T02:00:11.917Z","response_time":82,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","api","feedback-loop","llm","mcp","mcp-server","mcp-servers","mock","mocking-server","mocking-utility","models","openapi","swagger","training","training-data"],"created_at":"2025-06-29T11:06:03.117Z","updated_at":"2025-07-27T11:02:46.352Z","avatar_url":"https://github.com/MockLoop.png","language":"Python","readme":"\u003e **NOTE: We have [fully](https://github.com/MockLoop/mockloop-mcp/commit/2fe4f485b5a13346393a6d5ed0f8b3a4dded7bbb) implemented [SchemaPin](https://schemapin.org) 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** \n\n![MockLoop](logo.png \"MockLoop\")\n\n# MockLoop MCP - AI-Native Testing Platform\n\n[![PyPI version](https://img.shields.io/pypi/v/mockloop-mcp.svg)](https://pypi.org/project/mockloop-mcp/)\n[![Python versions](https://img.shields.io/pypi/pyversions/mockloop-mcp.svg)](https://pypi.org/project/mockloop-mcp/)\n[![Downloads](https://img.shields.io/pypi/dm/mockloop-mcp.svg)](https://pypi.org/project/mockloop-mcp/)\n[![License](https://img.shields.io/pypi/l/mockloop-mcp.svg)](https://github.com/mockloop/mockloop-mcp/blob/main/LICENSE)\n[![Tests](https://github.com/mockloop/mockloop-mcp/workflows/Tests/badge.svg)](https://github.com/mockloop/mockloop-mcp/actions)\n[![Documentation](https://img.shields.io/badge/docs-available-brightgreen.svg)](https://docs.mockloop.com)\n[![AI-Native](https://img.shields.io/badge/AI--Native-Testing-blue.svg)](https://docs.mockloop.com/ai-integration/overview/)\n[![MCP Compatible](https://img.shields.io/badge/MCP-Compatible-green.svg)](https://modelcontextprotocol.io/)\n\n**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.\n\n**🚀 Revolutionary Capabilities:** 5 AI Prompts • 15 Scenario Resources • 16 Testing Tools • 10 Context Tools • 4 Core Tools • Complete MCP Integration\n\n**📚 Documentation:** https://docs.mockloop.com  \n**📦 PyPI Package:** https://pypi.org/project/mockloop-mcp/  \n**🐙 GitHub Repository:** https://github.com/mockloop/mockloop-mcp\n\n## 🌟 What Makes MockLoop MCP Revolutionary?\n\nMockLoop MCP represents a paradigm shift in API testing, introducing the world's first **AI-native testing architecture** that combines:\n\n- **🤖 AI-Driven Test Generation**: 5 specialized MCP prompts for intelligent scenario creation  \n- **📦 Community Scenario Packs**: 15 curated testing resources with community architecture  \n- **⚡ Automated Test Execution**: 30 comprehensive MCP tools for complete testing workflows (16 testing + 10 context + 4 core)  \n- **🔄 Stateful Testing**: Advanced context management with GlobalContext and AgentContext  \n- **📊 Enterprise Compliance**: Complete audit logging and regulatory compliance tracking  \n- **🏗️ Dual-Port Architecture**: Eliminates /admin path conflicts with separate mocked API and admin ports\n\n## 🎯 Core AI-Native Architecture\n\n### MCP Audit Logging\n**Enterprise-grade compliance and regulatory tracking**  \n- Complete request/response audit trails  \n- Regulatory compliance monitoring  \n- Performance metrics and analytics  \n- Security event logging  \n\n### MCP Prompts (5 AI-Driven Capabilities)  \n**Intelligent scenario generation powered by AI**  \n- [`analyze_openapi_for_testing`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_prompts.py#L301) - Comprehensive API analysis for testing strategies\n- [`generate_scenario_config`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_prompts.py#L426) - Dynamic test scenario configuration\n- [`optimize_scenario_for_load`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_prompts.py#L521) - Load testing optimization\n- [`generate_error_scenarios`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_prompts.py#L633) - Error condition simulation\n- [`generate_security_test_scenarios`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_prompts.py#L732) - Security vulnerability testing\n\n### MCP Resources (15 Scenario Packs)  \n**Community-driven testing scenarios with advanced architecture**  \n- **Load Testing Scenarios**: High-volume traffic simulation  \n- **Error Simulation Packs**: Comprehensive error condition testing  \n- **Security Testing Suites**: Vulnerability assessment scenarios   \n- **Performance Benchmarks**: Standardized performance testing  \n- **Integration Test Packs**: Cross-service testing scenarios  \n- **Community Architecture**: Collaborative scenario sharing and validation  \n\n### MCP Tools (16 Automated Testing Tools)  \n**Complete automated test execution capabilities**  \n\n#### Scenario Management (4 tools)  \n- [`validate_scenario_config`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L174) - Scenario validation and verification\n- [`deploy_scenario`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L267) - Automated scenario deployment\n- [`switch_scenario`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L385) - Dynamic scenario switching\n- [`list_active_scenarios`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L476) - Active scenario monitoring\n\n#### Test Execution (4 tools)  \n- [`execute_test_plan`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L577) - Comprehensive test plan execution\n- [`run_test_iteration`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L816) - Individual test iteration management\n- [`run_load_test`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L945) - Load testing execution\n- [`run_security_test`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2019) - Security testing automation\n\n#### Analysis \u0026 Reporting (4 tools)\n- [`analyze_test_results`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2142) - Intelligent test result analysis\n- [`generate_test_report`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2225) - Comprehensive reporting\n- [`compare_test_runs`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2310) - Test run comparison and trends\n- [`get_performance_metrics`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2418) - Performance metrics collection\n\n#### Workflow Management (4 tools)\n- [`create_test_session`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2524) - Test session initialization\n- [`end_test_session`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2579) - Session cleanup and finalization\n- [`schedule_test_suite`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2646) - Automated test scheduling\n- [`monitor_test_progress`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_tools.py#L2702) - Real-time progress monitoring\n\n### MCP Context Management (10 Stateful Workflow Tools)\n**Advanced state management for complex testing workflows**\n\n#### Context Creation \u0026 Management\n- [`create_test_session_context`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1431) - Test session state management\n- [`create_workflow_context`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1447) - Complex workflow orchestration\n- [`create_agent_context`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1463) - AI agent state management\n\n#### Data Management\n- [`get_context_data`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1477) - Context data retrieval\n- [`update_context_data`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1486) - Dynamic context updates\n- [`list_contexts_by_type`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1508) - Context discovery and listing\n\n#### Snapshot \u0026 Recovery\n- [`create_context_snapshot`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1494) - State snapshot creation\n- [`restore_context_snapshot`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1502) - State rollback capabilities\n\n#### Global Context\n- [`get_global_context_data`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1516) - Cross-session data sharing\n- [`update_global_context_data`](https://github.com/MockLoop/mockloop-mcp/blob/main/src/mockloop_mcp/mcp_context.py#L1523) - Global state management\n\n## 🚀 Quick Start\n\nGet started with the world's most advanced AI-native testing platform:\n\n```bash\n# 1. Install MockLoop MCP\npip install mockloop-mcp\n\n# 2. Verify installation\nmockloop-mcp --version\n\n# 3. Configure with your MCP client (Cline, Claude Desktop, etc.)\n# See configuration examples below\n```\n\n## 📋 Prerequisites\n\n- Python 3.10+\n- Pip package manager\n- Docker and Docker Compose (for containerized mock servers)\n- An MCP-compatible client (Cline, Claude Desktop, etc.)\n\n## 🔧 Installation\n\n### Option 1: Install from PyPI (Recommended)\n\n```bash\n# Install the latest stable version\npip install mockloop-mcp\n\n# Or install with optional dependencies\npip install mockloop-mcp[dev]   # Development tools\npip install mockloop-mcp[docs]  # Documentation tools\npip install mockloop-mcp[all]   # All optional dependencies\n\n# Verify installation\nmockloop-mcp --version\n```\n\n### Option 2: Development Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/mockloop/mockloop-mcp.git\ncd mockloop-mcp\n\n# Create and activate virtual environment\npython3 -m venv .venv\nsource .venv/bin/activate  # On Windows: .venv\\Scripts\\activate\n\n# Install in development mode\npip install -e \".[dev]\"\n```\n\n## ⚙️ Configuration\n\n### MCP Client Configuration\n\n#### Cline (VS Code Extension)\n\nAdd to your Cline MCP settings file:\n\n```json\n{\n  \"mcpServers\": {\n    \"MockLoopLocal\": {\n      \"autoApprove\": [],\n      \"disabled\": false,\n      \"timeout\": 60,\n      \"command\": \"mockloop-mcp\",\n      \"args\": [],\n      \"transportType\": \"stdio\"\n    }\n  }\n}\n```\n\n#### Claude Desktop\n\nAdd to your Claude Desktop configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"mockloop\": {\n      \"command\": \"mockloop-mcp\",\n      \"args\": []\n    }\n  }\n}\n```\n\n#### Virtual Environment Installations\n\nFor virtual environment installations, use the full Python path:\n\n```json\n{\n  \"mcpServers\": {\n    \"MockLoopLocal\": {\n      \"command\": \"/path/to/your/venv/bin/python\",\n      \"args\": [\"-m\", \"mockloop_mcp\"],\n      \"transportType\": \"stdio\"\n    }\n  }\n}\n```\n\n## 🛠️ Available MCP Tools\n\n### Core Mock Generation\n\n#### `generate_mock_api`\nGenerate sophisticated FastAPI mock servers with dual-port architecture.\n\n**Parameters:**\n- `spec_url_or_path` (string, required): API specification URL or local file path\n- `output_dir_name` (string, optional): Output directory name\n- `auth_enabled` (boolean, optional): Enable authentication middleware (default: true)\n- `webhooks_enabled` (boolean, optional): Enable webhook support (default: true)\n- `admin_ui_enabled` (boolean, optional): Enable admin UI (default: true)\n- `storage_enabled` (boolean, optional): Enable storage functionality (default: true)\n\n**Revolutionary Dual-Port Architecture:**\n- **Mocked API Port**: Serves your API endpoints (default: 8000)\n- **Admin UI Port**: Separate admin interface (default: 8001)\n- **Conflict Resolution**: Eliminates /admin path conflicts in OpenAPI specs\n- **Enhanced Security**: Port-based access control and isolation\n\n### Advanced Analytics\n\n#### `query_mock_logs`\nQuery and analyze request logs with AI-powered insights.\n\n**Parameters:**\n- `server_url` (string, required): Mock server URL\n- `limit` (integer, optional): Maximum logs to return (default: 100)\n- `offset` (integer, optional): Pagination offset (default: 0)\n- `method` (string, optional): Filter by HTTP method\n- `path_pattern` (string, optional): Regex pattern for path filtering\n- `time_from` (string, optional): Start time filter (ISO format)\n- `time_to` (string, optional): End time filter (ISO format)\n- `include_admin` (boolean, optional): Include admin requests (default: false)\n- `analyze` (boolean, optional): Perform AI analysis (default: true)\n\n**AI-Powered Analysis:**\n- Performance metrics (P95/P99 response times)\n- Error rate analysis and categorization\n- Traffic pattern detection\n- Automated debugging recommendations\n- Session correlation and tracking\n\n#### `discover_mock_servers`\nIntelligent server discovery with dual-port architecture support.\n\n**Parameters:**\n- `ports` (array, optional): Ports to scan (default: common ports)\n- `check_health` (boolean, optional): Perform health checks (default: true)\n- `include_generated` (boolean, optional): Include generated mocks (default: true)\n\n**Advanced Discovery:**\n- Automatic architecture detection (single-port vs dual-port)\n- Health status monitoring\n- Server correlation and matching\n- Port usage analysis\n\n#### `manage_mock_data`\nDynamic response management without server restart.\n\n**Parameters:**\n- `server_url` (string, required): Mock server URL\n- `operation` (string, required): Operation type (\"update_response\", \"create_scenario\", \"switch_scenario\", \"list_scenarios\")\n- `endpoint_path` (string, optional): API endpoint path\n- `response_data` (object, optional): New response data\n- `scenario_name` (string, optional): Scenario name\n- `scenario_config` (object, optional): Scenario configuration\n\n**Dynamic Capabilities:**\n- Real-time response updates\n- Scenario-based testing\n- Runtime configuration management\n- Zero-downtime modifications\n\n## 🌐 MCP Proxy Functionality\n\nMockLoop MCP includes revolutionary proxy capabilities that enable seamless switching between mock and live API environments. This powerful feature transforms your testing workflow by providing:\n\n### Core Proxy Capabilities\n\n- **🔄 Seamless Mode Switching**: Transition between mock, proxy, and hybrid modes without code changes\n- **🎯 Intelligent Routing**: Smart request routing based on configurable rules and conditions\n- **🔐 Universal Authentication**: Support for API Key, Bearer Token, Basic Auth, and OAuth2\n- **📊 Response Comparison**: Automated comparison between mock and live API responses\n- **⚡ Zero-Downtime Switching**: Change modes dynamically without service interruption\n\n### Operational Modes\n\n#### Mock Mode (`MOCK`)\n- All requests handled by generated mock responses\n- Predictable, consistent testing environment\n- Ideal for early development and isolated testing\n- No external dependencies or network calls\n\n#### Proxy Mode (`PROXY`)\n- All requests forwarded to live API endpoints\n- Real-time data and authentic responses\n- Full integration testing capabilities\n- Network-dependent operation with live credentials\n\n#### Hybrid Mode (`HYBRID`)\n- Intelligent routing between mock and proxy based on rules\n- Conditional switching based on request patterns, headers, or parameters\n- Gradual migration from mock to live environments\n- A/B testing and selective endpoint proxying\n\n### Quick Start Example\n\n```python\nfrom mockloop_mcp.mcp_tools import create_mcp_plugin\n\n# Create a proxy-enabled plugin\nplugin_result = await create_mcp_plugin(\n    spec_url_or_path=\"https://api.example.com/openapi.json\",\n    mode=\"hybrid\",  # Start with hybrid mode\n    plugin_name=\"example_api\",\n    target_url=\"https://api.example.com\",\n    auth_config={\n        \"auth_type\": \"bearer_token\",\n        \"credentials\": {\"token\": \"your-token\"}\n    },\n    routing_rules=[\n        {\n            \"pattern\": \"/api/critical/*\",\n            \"mode\": \"proxy\",  # Critical endpoints use live API\n            \"priority\": 10\n        },\n        {\n            \"pattern\": \"/api/dev/*\",\n            \"mode\": \"mock\",   # Development endpoints use mocks\n            \"priority\": 5\n        }\n    ]\n)\n```\n\n### Advanced Features\n\n- **🔍 Response Validation**: Compare mock vs live responses for consistency\n- **📈 Performance Monitoring**: Track response times and throughput across modes\n- **🛡️ Error Handling**: Graceful fallback mechanisms and retry policies\n- **🎛️ Dynamic Configuration**: Runtime mode switching and rule updates\n- **📋 Audit Logging**: Complete request/response tracking across all modes\n\n### Authentication Support\n\nThe proxy system supports comprehensive authentication schemes:\n\n- **API Key**: Header, query parameter, or cookie-based authentication\n- **Bearer Token**: OAuth2 and JWT token support\n- **Basic Auth**: Username/password combinations\n- **OAuth2**: Full OAuth2 flow with token refresh\n- **Custom**: Extensible authentication handlers for proprietary schemes\n\n### Use Cases\n\n- **Development Workflow**: Start with mocks, gradually introduce live APIs\n- **Integration Testing**: Validate against real services while maintaining test isolation\n- **Performance Testing**: Compare mock vs live API performance characteristics\n- **Staging Validation**: Ensure mock responses match production API behavior\n- **Hybrid Deployments**: Route critical operations to live APIs, others to mocks\n\n**📚 Complete Guide**: For detailed configuration, examples, and best practices, see the [MCP Proxy Guide](docs/guides/mcp-proxy-guide.md).\n## 🤖 AI Framework Integration\n\nMockLoop MCP provides native integration with popular AI frameworks:\n\n### LangGraph Integration\n\n```python\nfrom langgraph.graph import StateGraph, END\nfrom mockloop_mcp import MockLoopClient\n\n# Initialize MockLoop client\nmockloop = MockLoopClient()\n\ndef setup_ai_testing(state):\n    \"\"\"AI-driven test setup\"\"\"\n    # Generate mock API with AI analysis\n    result = mockloop.generate_mock_api(\n        spec_url_or_path=\"https://api.example.com/openapi.json\",\n        output_dir_name=\"ai_test_environment\"\n    )\n    \n    # Use AI prompts for scenario generation\n    scenarios = mockloop.analyze_openapi_for_testing(\n        api_spec=state[\"api_spec\"],\n        analysis_depth=\"comprehensive\",\n        include_security_tests=True\n    )\n    \n    state[\"mock_server_url\"] = \"http://localhost:8000\"\n    state[\"test_scenarios\"] = scenarios\n    return state\n\ndef execute_ai_tests(state):\n    \"\"\"Execute AI-generated test scenarios\"\"\"\n    # Deploy AI-generated scenarios\n    for scenario in state[\"test_scenarios\"]:\n        mockloop.deploy_scenario(\n            server_url=state[\"mock_server_url\"],\n            scenario_config=scenario\n        )\n        \n        # Execute load tests with AI optimization\n        results = mockloop.run_load_test(\n            server_url=state[\"mock_server_url\"],\n            scenario_name=scenario[\"name\"],\n            duration=300,\n            concurrent_users=100\n        )\n        \n        # AI-powered result analysis\n        analysis = mockloop.analyze_test_results(\n            test_results=results,\n            include_recommendations=True\n        )\n        \n        state[\"test_results\"].append(analysis)\n    \n    return state\n\n# Build AI-native testing workflow\nworkflow = StateGraph(dict)\nworkflow.add_node(\"setup_ai_testing\", setup_ai_testing)\nworkflow.add_node(\"execute_ai_tests\", execute_ai_tests)\nworkflow.set_entry_point(\"setup_ai_testing\")\nworkflow.add_edge(\"setup_ai_testing\", \"execute_ai_tests\")\nworkflow.add_edge(\"execute_ai_tests\", END)\n\napp = workflow.compile()\n```\n\n### CrewAI Multi-Agent Testing\n\n```python\nfrom crewai import Agent, Task, Crew\nfrom mockloop_mcp import MockLoopClient\n\n# Initialize MockLoop client\nmockloop = MockLoopClient()\n\n# AI Testing Specialist Agent\napi_testing_agent = Agent(\n    role='AI API Testing Specialist',\n    goal='Generate and execute comprehensive AI-driven API tests',\n    backstory='Expert in AI-native testing with MockLoop MCP integration',\n    tools=[\n        mockloop.generate_mock_api,\n        mockloop.analyze_openapi_for_testing,\n        mockloop.generate_scenario_config\n    ]\n)\n\n# Performance Analysis Agent\nperformance_agent = Agent(\n    role='AI Performance Analyst',\n    goal='Analyze API performance with AI-powered insights',\n    backstory='Specialist in AI-driven performance analysis and optimization',\n    tools=[\n        mockloop.run_load_test,\n        mockloop.get_performance_metrics,\n        mockloop.analyze_test_results\n    ]\n)\n\n# Security Testing Agent\nsecurity_agent = Agent(\n    role='AI Security Testing Expert',\n    goal='Conduct AI-driven security testing and vulnerability assessment',\n    backstory='Expert in AI-powered security testing methodologies',\n    tools=[\n        mockloop.generate_security_test_scenarios,\n        mockloop.run_security_test,\n        mockloop.compare_test_runs\n    ]\n)\n\n# Define AI-driven tasks\nai_setup_task = Task(\n    description='Generate AI-native mock API with comprehensive testing scenarios',\n    agent=api_testing_agent,\n    expected_output='Mock server with AI-generated test scenarios deployed'\n)\n\nperformance_task = Task(\n    description='Execute AI-optimized performance testing and analysis',\n    agent=performance_agent,\n    expected_output='Comprehensive performance analysis with AI recommendations'\n)\n\nsecurity_task = Task(\n    description='Conduct AI-driven security testing and vulnerability assessment',\n    agent=security_agent,\n    expected_output='Security test results with AI-powered threat analysis'\n)\n\n# Create AI testing crew\nai_testing_crew = Crew(\n    agents=[api_testing_agent, performance_agent, security_agent],\n    tasks=[ai_setup_task, performance_task, security_task],\n    verbose=True\n)\n\n# Execute AI-native testing workflow\nresults = ai_testing_crew.kickoff()\n```\n\n### LangChain AI Testing Tools\n\n```python\nfrom langchain.agents import Tool, AgentExecutor, create_react_agent\nfrom langchain.prompts import PromptTemplate\nfrom langchain_openai import ChatOpenAI\nfrom mockloop_mcp import MockLoopClient\n\n# Initialize MockLoop client\nmockloop = MockLoopClient()\n\n# AI-Native Testing Tools\ndef ai_generate_mock_api(spec_path: str) -\u003e str:\n    \"\"\"Generate AI-enhanced mock API with intelligent scenarios\"\"\"\n    # Generate mock API\n    result = mockloop.generate_mock_api(spec_url_or_path=spec_path)\n    \n    # Use AI to analyze and enhance\n    analysis = mockloop.analyze_openapi_for_testing(\n        api_spec=spec_path,\n        analysis_depth=\"comprehensive\",\n        include_security_tests=True\n    )\n    \n    return f\"AI-enhanced mock API generated: {result}\\nAI Analysis: {analysis['summary']}\"\n\ndef ai_execute_testing_workflow(server_url: str) -\u003e str:\n    \"\"\"Execute comprehensive AI-driven testing workflow\"\"\"\n    # Create test session context\n    session = mockloop.create_test_session_context(\n        session_name=\"ai_testing_session\",\n        configuration={\"ai_enhanced\": True}\n    )\n    \n    # Generate and deploy AI scenarios\n    scenarios = mockloop.generate_scenario_config(\n        api_spec=server_url,\n        scenario_types=[\"load\", \"error\", \"security\"],\n        ai_optimization=True\n    )\n    \n    results = []\n    for scenario in scenarios:\n        # Deploy scenario\n        mockloop.deploy_scenario(\n            server_url=server_url,\n            scenario_config=scenario\n        )\n        \n        # Execute tests with AI monitoring\n        test_result = mockloop.execute_test_plan(\n            server_url=server_url,\n            test_plan=scenario[\"test_plan\"],\n            ai_monitoring=True\n        )\n        \n        results.append(test_result)\n    \n    # AI-powered analysis\n    analysis = mockloop.analyze_test_results(\n        test_results=results,\n        include_recommendations=True,\n        ai_insights=True\n    )\n    \n    return f\"AI testing workflow completed: {analysis['summary']}\"\n\n# Create LangChain tools\nai_testing_tools = [\n    Tool(\n        name=\"AIGenerateMockAPI\",\n        func=ai_generate_mock_api,\n        description=\"Generate AI-enhanced mock API with intelligent testing scenarios\"\n    ),\n    Tool(\n        name=\"AIExecuteTestingWorkflow\",\n        func=ai_execute_testing_workflow,\n        description=\"Execute comprehensive AI-driven testing workflow with intelligent analysis\"\n    )\n]\n\n# Create AI testing agent\nllm = ChatOpenAI(temperature=0)\nai_testing_prompt = PromptTemplate.from_template(\"\"\"\nYou are an AI-native testing assistant powered by MockLoop MCP.\nYou have access to revolutionary AI-driven testing capabilities including:\n- AI-powered scenario generation\n- Intelligent test execution\n- Advanced performance analysis\n- Security vulnerability assessment\n- Stateful workflow management\n\nTools available: {tools}\nTool names: {tool_names}\n\nQuestion: {input}\n{agent_scratchpad}\n\"\"\")\n\nagent = create_react_agent(llm, ai_testing_tools, ai_testing_prompt)\nagent_executor = AgentExecutor(agent=agent, tools=ai_testing_tools, verbose=True)\n\n# Execute AI-native testing\nresponse = agent_executor.invoke({\n    \"input\": \"Generate a comprehensive AI-driven testing environment for a REST API and execute full testing workflow\"\n})\n```\n\n## 🏗️ Dual-Port Architecture\n\nMockLoop MCP introduces a revolutionary **dual-port architecture** that eliminates common conflicts and enhances security:\n\n### Architecture Benefits\n\n- **🔒 Enhanced Security**: Complete separation of mocked API and admin functionality\n- **⚡ Zero Conflicts**: Eliminates /admin path conflicts in OpenAPI specifications\n- **📊 Clean Analytics**: Admin calls don't appear in mocked API metrics\n- **🔄 Independent Scaling**: Scale mocked API and admin services separately\n- **🛡️ Port-Based Access Control**: Enhanced security through network isolation\n\n### Port Configuration\n\n```python\n# Generate mock with dual-port architecture\nresult = mockloop.generate_mock_api(\n    spec_url_or_path=\"https://api.example.com/openapi.json\",\n    business_port=8000,  # Mocked API port\n    admin_port=8001,     # Admin UI port\n    admin_ui_enabled=True\n)\n```\n\n### Access Points\n\n- **Mocked API**: `http://localhost:8000` - Your API endpoints\n- **Admin UI**: `http://localhost:8001` - Management interface\n- **API Documentation**: `http://localhost:8000/docs` - Interactive Swagger UI\n- **Health Check**: `http://localhost:8000/health` - Server status\n\n## 📊 Enterprise Features\n\n### Compliance \u0026 Audit Logging\n\nMockLoop MCP provides enterprise-grade compliance features:\n\n- **Complete Audit Trails**: Every request/response logged with metadata\n- **Regulatory Compliance**: GDPR, SOX, HIPAA compliance support\n- **Performance Metrics**: P95/P99 response times, error rates\n- **Security Monitoring**: Threat detection and analysis\n- **Session Tracking**: Cross-request correlation and analysis\n\n### Advanced Analytics\n\n- **AI-Powered Insights**: Intelligent analysis and recommendations\n- **Traffic Pattern Detection**: Automated anomaly detection\n- **Performance Optimization**: AI-driven performance recommendations\n- **Error Analysis**: Intelligent error categorization and resolution\n- **Trend Analysis**: Historical performance and usage trends\n\n## 🔄 Stateful Testing Workflows\n\nMockLoop MCP supports complex, stateful testing workflows through advanced context management:\n\n### Context Types\n\n- **Test Session Context**: Maintain state across test executions\n- **Workflow Context**: Complex multi-step testing orchestration\n- **Agent Context**: AI agent state management and coordination\n- **Global Context**: Cross-session data sharing and persistence\n\n### Example: Stateful E-commerce Testing\n\n```python\n# Create test session context\nsession = mockloop.create_test_session_context(\n    session_name=\"ecommerce_integration_test\",\n    configuration={\n        \"test_type\": \"integration\",\n        \"environment\": \"staging\",\n        \"ai_enhanced\": True\n    }\n)\n\n# Create workflow context for multi-step testing\nworkflow = mockloop.create_workflow_context(\n    workflow_name=\"user_journey_test\",\n    parent_context=session[\"context_id\"],\n    steps=[\n        \"user_registration\",\n        \"product_browsing\",\n        \"cart_management\",\n        \"checkout_process\",\n        \"order_fulfillment\"\n    ]\n)\n\n# Execute stateful test workflow\nfor step in workflow[\"steps\"]:\n    # Update context with step data\n    mockloop.update_context_data(\n        context_id=workflow[\"context_id\"],\n        data={\"current_step\": step, \"timestamp\": datetime.now()}\n    )\n    \n    # Execute step-specific tests\n    test_result = mockloop.execute_test_plan(\n        server_url=\"http://localhost:8000\",\n        test_plan=f\"{step}_test_plan\",\n        context_id=workflow[\"context_id\"]\n    )\n    \n    # Create snapshot for rollback capability\n    snapshot = mockloop.create_context_snapshot(\n        context_id=workflow[\"context_id\"],\n        snapshot_name=f\"{step}_completion\"\n    )\n\n# Analyze complete workflow results\nfinal_analysis = mockloop.analyze_test_results(\n    test_results=workflow[\"results\"],\n    context_id=workflow[\"context_id\"],\n    include_recommendations=True\n)\n```\n\n## 🚀 Running Generated Mock Servers\n\n### Using Docker Compose (Recommended)\n\n```bash\n# Navigate to generated mock directory\ncd generated_mocks/your_api_mock\n\n# Start with dual-port architecture\ndocker-compose up --build\n\n# Access points:\n# Mocked API: http://localhost:8000\n# Admin UI: http://localhost:8001\n```\n\n### Using Uvicorn Directly\n\n```bash\n# Install dependencies\npip install -r requirements_mock.txt\n\n# Start the mock server\nuvicorn main:app --reload --port 8000\n```\n\n### Enhanced Features Access\n\n- **Admin UI**: `http://localhost:8001` - Enhanced management interface\n- **API Documentation**: `http://localhost:8000/docs` - Interactive Swagger UI\n- **Health Check**: `http://localhost:8000/health` - Server status and metrics\n- **Log Analytics**: `http://localhost:8001/api/logs/search` - Advanced log querying\n- **Performance Metrics**: `http://localhost:8001/api/logs/analyze` - AI-powered insights\n- **Scenario Management**: `http://localhost:8001/api/mock-data/scenarios` - Dynamic testing\n\n## 📈 Performance \u0026 Scalability\n\nMockLoop MCP is designed for enterprise-scale performance:\n\n### Performance Metrics\n\n- **Response Times**: P50, P95, P99 percentile tracking\n- **Throughput**: Requests per second monitoring\n- **Error Rates**: Comprehensive error analysis\n- **Resource Usage**: Memory, CPU, and network monitoring\n- **Concurrency**: Multi-user load testing support\n\n### Scalability Features\n\n- **Horizontal Scaling**: Multi-instance deployment support\n- **Load Balancing**: Built-in load balancing capabilities\n- **Caching**: Intelligent response caching\n- **Database Optimization**: Efficient SQLite and PostgreSQL support\n- **Container Orchestration**: Kubernetes and Docker Swarm ready\n\n## 🔒 Security Features\n\n### Built-in Security\n\n- **Authentication Middleware**: Configurable auth mechanisms\n- **Rate Limiting**: Prevent abuse and DoS attacks\n- **Input Validation**: Comprehensive request validation\n- **Security Headers**: CORS, CSP, and security headers\n- **Audit Logging**: Complete security event logging\n\n### Security Testing\n\n- **Vulnerability Assessment**: AI-powered security testing\n- **Penetration Testing**: Automated security scenario generation\n- **Compliance Checking**: Security standard compliance verification\n- **Threat Modeling**: AI-driven threat analysis\n- **Security Reporting**: Comprehensive security analytics\n\n## 🔐 SchemaPin Integration - Cryptographic Schema Verification\n\nMockLoop 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.\n\n### Revolutionary Security Enhancement\n\nSchemaPin integration transforms MockLoop MCP into the most secure MCP testing platform by providing:\n\n- **🔐 Cryptographic Verification**: ECDSA P-256 signatures ensure schema integrity\n- **🔑 TOFU Key Pinning**: Automatic key discovery and pinning for trusted domains\n- **📋 Policy Enforcement**: Configurable security policies (enforce/warn/log modes)\n- **📊 Comprehensive Auditing**: Complete verification logs for compliance\n- **🔄 Graceful Fallback**: Works with or without SchemaPin library\n- **🏗️ Hybrid Architecture**: Seamless integration with existing MockLoop systems\n\n### Quick Start Configuration\n\n```python\nfrom mockloop_mcp.schemapin import SchemaPinConfig, SchemaVerificationInterceptor\n\n# Basic configuration\nconfig = SchemaPinConfig(\n    enabled=True,\n    policy_mode=\"warn\",  # enforce, warn, or log\n    auto_pin_keys=False,\n    trusted_domains=[\"api.example.com\"],\n    interactive_mode=False\n)\n\n# Initialize verification\ninterceptor = SchemaVerificationInterceptor(config)\n\n# Verify tool schema\nresult = await interceptor.verify_tool_schema(\n    tool_name=\"database_query\",\n    schema=tool_schema,\n    signature=\"base64_encoded_signature\",\n    domain=\"api.example.com\"\n)\n\nif result.valid:\n    print(\"✓ Schema verification successful\")\nelse:\n    print(f\"✗ Verification failed: {result.error}\")\n```\n\n### Production Configuration\n\n```python\n# Production-ready configuration\nconfig = SchemaPinConfig(\n    enabled=True,\n    policy_mode=\"enforce\",  # Block execution on verification failure\n    auto_pin_keys=True,     # Auto-pin keys for trusted domains\n    key_pin_storage_path=\"/secure/path/keys.db\",\n    discovery_timeout=60,\n    cache_ttl=7200,\n    trusted_domains=[\n        \"api.corp.com\",\n        \"tools.internal.com\"\n    ],\n    well_known_endpoints={\n        \"api.corp.com\": \"https://api.corp.com/.well-known/schemapin.json\"\n    },\n    revocation_check=True,\n    interactive_mode=False\n)\n```\n\n### Security Benefits\n\n#### MCP Rug Pull Protection\nSchemaPin prevents malicious actors from modifying tool schemas without detection:\n\n- **Cryptographic Signatures**: Every tool schema is cryptographically signed\n- **Key Pinning**: TOFU model prevents man-in-the-middle attacks\n- **Audit Trails**: Complete verification logs for security analysis\n- **Policy Enforcement**: Configurable responses to verification failures\n\n#### Compliance \u0026 Governance\n- **Regulatory Compliance**: Audit logs support GDPR, SOX, HIPAA requirements\n- **Enterprise Security**: Integration with existing security frameworks\n- **Risk Management**: Configurable security policies for different environments\n- **Threat Detection**: Automated detection of schema tampering attempts\n\n### Integration Examples\n\n#### Basic Tool Verification\n```python\n# Verify a single tool\nfrom mockloop_mcp.schemapin import SchemaVerificationInterceptor\n\ninterceptor = SchemaVerificationInterceptor(config)\nresult = await interceptor.verify_tool_schema(\n    \"api_call\", tool_schema, signature, \"api.example.com\"\n)\n```\n\n#### Batch Verification\n```python\n# Verify multiple tools efficiently\nfrom mockloop_mcp.schemapin import SchemaPinWorkflowManager\n\nworkflow = SchemaPinWorkflowManager(config)\nresults = await workflow.verify_tool_batch([\n    {\"name\": \"tool1\", \"schema\": schema1, \"signature\": sig1, \"domain\": \"api.com\"},\n    {\"name\": \"tool2\", \"schema\": schema2, \"signature\": sig2, \"domain\": \"api.com\"}\n])\n```\n\n#### MCP Proxy Integration\n```python\n# Integrate with MCP proxy for seamless security\nclass SecureMCPProxy:\n    def __init__(self, config):\n        self.interceptor = SchemaVerificationInterceptor(config)\n    \n    async def proxy_tool_request(self, tool_name, schema, signature, domain, data):\n        # Verify schema before execution\n        result = await self.interceptor.verify_tool_schema(\n            tool_name, schema, signature, domain\n        )\n        \n        if not result.valid:\n            return {\"error\": \"Schema verification failed\"}\n        \n        # Execute tool with verified schema\n        return await self.execute_tool(tool_name, data)\n```\n\n### Policy Modes\n\n#### Enforce Mode\n```python\nconfig = SchemaPinConfig(policy_mode=\"enforce\")\n# Blocks execution on verification failure\n# Recommended for production critical tools\n```\n\n#### Warn Mode\n```python\nconfig = SchemaPinConfig(policy_mode=\"warn\")\n# Logs warnings but allows execution\n# Recommended for gradual rollout\n```\n\n#### Log Mode\n```python\nconfig = SchemaPinConfig(policy_mode=\"log\")\n# Logs events without blocking\n# Recommended for monitoring and testing\n```\n\n### Key Management\n\n#### Trust-On-First-Use (TOFU)\n```python\n# Automatic key discovery and pinning\nkey_manager = KeyPinningManager(\"keys.db\")\n\n# Pin key for trusted tool\nsuccess = key_manager.pin_key(\n    tool_id=\"api.example.com/database_query\",\n    domain=\"api.example.com\",\n    public_key_pem=discovered_key,\n    metadata={\"developer\": \"Example Corp\"}\n)\n\n# Check if key is pinned\nif key_manager.is_key_pinned(\"api.example.com/database_query\"):\n    print(\"Key is pinned and trusted\")\n```\n\n#### Key Discovery\nSchemaPin automatically discovers public keys via `.well-known` endpoints:\n```\nhttps://api.example.com/.well-known/schemapin.json\n```\n\nExpected format:\n```json\n{\n  \"public_key\": \"-----BEGIN PUBLIC KEY-----\\n...\\n-----END PUBLIC KEY-----\",\n  \"algorithm\": \"ES256\",\n  \"created_at\": \"2023-01-01T00:00:00Z\"\n}\n```\n\n### Audit \u0026 Compliance\n\n#### Comprehensive Logging\n```python\nfrom mockloop_mcp.schemapin import SchemaPinAuditLogger\n\naudit_logger = SchemaPinAuditLogger(\"audit.db\")\n\n# Verification events are automatically logged\nstats = audit_logger.get_verification_stats()\nprint(f\"Total verifications: {stats['total_verifications']}\")\nprint(f\"Success rate: {stats['successful_verifications'] / stats['total_verifications'] * 100:.1f}%\")\n```\n\n#### Compliance Reporting\n```python\n# Generate compliance reports\nfrom mockloop_mcp.mcp_compliance import MCPComplianceReporter\n\nreporter = MCPComplianceReporter(\"audit.db\")\nreport = reporter.generate_schemapin_compliance_report()\n\nprint(f\"Compliance score: {report['compliance_score']:.1f}%\")\nprint(f\"Verification coverage: {report['verification_statistics']['unique_tools']} tools\")\n```\n\n### Documentation \u0026 Examples\n\n- **📚 Complete Integration Guide**: [`docs/guides/schemapin-integration.md`](docs/guides/schemapin-integration.md)\n- **🔧 Basic Usage Example**: [`examples/schemapin/basic_usage.py`](examples/schemapin/basic_usage.py)\n- **⚡ Advanced Patterns**: [`examples/schemapin/advanced_usage.py`](examples/schemapin/advanced_usage.py)\n- **🏗️ Architecture Documentation**: [`SchemaPin_MockLoop_Integration_Architecture.md`](SchemaPin_MockLoop_Integration_Architecture.md)\n- **🧪 Test Coverage**: 56 comprehensive tests (42 unit + 14 integration)\n\n### Migration for Existing Users\n\nSchemaPin integration is **completely backward compatible**:\n\n1. **Opt-in Configuration**: SchemaPin is disabled by default\n2. **No Breaking Changes**: Existing tools continue to work unchanged\n3. **Gradual Rollout**: Start with `log` mode, progress to `warn`, then `enforce`\n4. **Zero Downtime**: Enable verification without service interruption\n\n```python\n# Migration example: gradual rollout\n# Phase 1: Monitoring (log mode)\nconfig = SchemaPinConfig(enabled=True, policy_mode=\"log\")\n\n# Phase 2: Warnings (warn mode)\nconfig = SchemaPinConfig(enabled=True, policy_mode=\"warn\")\n\n# Phase 3: Enforcement (enforce mode)\nconfig = SchemaPinConfig(enabled=True, policy_mode=\"enforce\")\n```\n\n### Performance Impact\n\nSchemaPin is designed for minimal performance impact:\n\n- **Verification Time**: ~5-15ms per tool (cached results)\n- **Memory Usage**: \u003c10MB additional memory\n- **Network Overhead**: Key discovery only on first use\n- **Database Size**: ~1KB per pinned key\n\n### Use Cases\n\n#### Development Teams\n- **Secure Development**: Verify tool schemas during development\n- **Code Review**: Ensure schema integrity in pull requests\n- **Testing**: Validate tool behavior with verified schemas\n\n#### Enterprise Security\n- **Threat Prevention**: Block malicious schema modifications\n- **Compliance**: Meet regulatory requirements with audit trails\n- **Risk Management**: Configurable security policies\n- **Incident Response**: Detailed logs for security analysis\n\n#### DevOps \u0026 CI/CD\n- **Pipeline Security**: Verify schemas in deployment pipelines\n- **Environment Promotion**: Ensure schema consistency across environments\n- **Monitoring**: Continuous verification monitoring\n- **Automation**: Automated security policy enforcement\n\n## �️ Future Development\n\n### Upcoming Features 🚧\n\n#### Enhanced AI Capabilities\n- **Advanced ML Models**: Custom model training for API testing\n- **Predictive Analytics**: AI-powered failure prediction\n- **Intelligent Test Generation**: Self-improving test scenarios\n- **Natural Language Testing**: Plain English test descriptions\n\n#### Extended Protocol Support\n- **GraphQL Support**: Native GraphQL API testing\n- **gRPC Integration**: Protocol buffer testing support\n- **WebSocket Testing**: Real-time communication testing\n- **Event-Driven Testing**: Async and event-based API testing\n\n#### Enterprise Integration\n- **CI/CD Integration**: Native pipeline integration\n- **Monitoring Platforms**: Datadog, New Relic, Prometheus integration\n- **Identity Providers**: SSO and enterprise auth integration\n- **Compliance Frameworks**: Extended regulatory compliance support\n\n## 🤝 Contributing\n\nWe welcome contributions to MockLoop MCP! Please see our [Contributing Guidelines](docs/contributing/guidelines.md) for details.\n\n### Development Setup\n\n```bash\n# Fork and clone the repository\ngit clone https://github.com/your-username/mockloop-mcp.git\ncd mockloop-mcp\n\n# Create development environment\npython3 -m venv .venv\nsource .venv/bin/activate\n\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run tests\npytest tests/\n\n# Run quality checks\nruff check src/\nbandit -r src/\n```\n\n### Community\n\n- **GitHub Repository**: [mockloop/mockloop-mcp](https://github.com/mockloop/mockloop-mcp)\n- **Issues \u0026 Bug Reports**: [GitHub Issues](https://github.com/mockloop/mockloop-mcp/issues)\n- **Feature Requests**: [GitHub Issues](https://github.com/mockloop/mockloop-mcp/issues)\n- **Documentation**: [docs.mockloop.com](https://docs.mockloop.com)\n\n## 📄 License\n\nMockLoop MCP is licensed under the [MIT License](LICENSE).\n\n---\n\n## 🎉 Get Started Today!\n\nReady to revolutionize your API testing with the world's first AI-native testing platform?\n\n```bash\npip install mockloop-mcp\n```\n\n**Join the AI-native testing revolution** and experience the future of API testing with MockLoop MCP!\n\n**🚀 [Get Started Now](https://docs.mockloop.com/getting-started/installation/) →**\n","funding_links":[],"categories":["Developer Tools","🤖 AI/ML"],"sub_categories":["How to Submit"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmockloop%2Fmockloop-mcp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmockloop%2Fmockloop-mcp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmockloop%2Fmockloop-mcp/lists"}