Physics MCP Server

--- title: Experiment Orchestrator kind: reference header_svg: src: "/assets/svg/tool-orchestrator-hero.svg" static: "/assets/svg/tool-orchestrator-hero-static.svg" title: "Experiment Orchestrator" animate: true theme_variant: "auto" reduced_motion: "auto" --- {% assign header_svg = page.header_svg %} {% include header-svg.html %} # Experiment Orchestrator The Experiment Orchestrator is the crown jewel of Physics MCP—a unified digital physics lab that allows you to define, validate, execute, and publish complex multi-step physics experiments using Directed Acyclic Graphs (DAGs). ## Core Capabilities ### DAG Definition - **Visual Workflow**: Define experiments as connected graphs of tools - **Natural Language**: Describe experiments in plain English - **JSON Specification**: Programmatic definition for advanced users - **Template Library**: Pre-built workflows for common experiments ### Validation & Safety - **Cyclic Detection**: Ensure workflows don't have circular dependencies - **Schema Validation**: Verify all parameters are correct - **Resource Limits**: Prevent runaway computations - **Graphics Audit**: Ensure all visual outputs are properly declared ### Execution & Monitoring - **Parallel Processing**: Run independent steps simultaneously - **Progress Tracking**: Real-time monitoring of experiment progress - **Error Handling**: Graceful failure recovery and retry logic - **Caching**: Avoid redundant computations with intelligent caching ### Publication & Sharing - **Automatic Reports**: Generate publication-ready PDFs with figures - **Artifact Management**: Track and version all generated content - **Collaboration**: Share experiments with colleagues and students - **Reproducibility**: Exact reproduction of any previous experiment ## Usage Examples ### Define Simple DAG ```json { "tool": "experiment_orchestrator", "params": { "action": "define_dag", "name": "Quantum Oscillator Analysis", "description": "Analyze energy levels of quantum harmonic oscillator", "nodes": [ { "id": "calculate_levels", "tool": "quantum", "params": { "problem": "sho", "params": {"mass": 1.0, "frequency": 2.0} } }, { "id": "plot_levels", "tool": "plot", "params": { "plot_type": "function_2d", "f": "from:calculate_levels.energy_expression" }, "dependencies": ["calculate_levels"] } ] } } ``` ### Natural Language Definition ```json { "tool": "experiment_orchestrator", "params": { "action": "define_dag", "description": "First calculate the energy levels of a quantum harmonic oscillator, then plot the wave functions for the first three states, and finally create an animation showing how the probability density changes over time" } } ``` ### Execute Complete Experiment ```json { "tool": "experiment_orchestrator", "params": { "action": "run_dag", "dag_id": "quantum_oscillator_analysis", "execution_policy": "local_first", "parallel_execution": true, "cache_enabled": true } } ``` ### Publish Results ```json { "tool": "experiment_orchestrator", "params": { "action": "publish_report", "dag_id": "quantum_oscillator_analysis", "title": "Quantum Harmonic Oscillator: Complete Analysis", "authors": ["Dr. Smith", "Student Name"], "abstract": "Comprehensive analysis of quantum harmonic oscillator using Physics MCP", "include_artifacts": true, "format": "pdf" } } ``` ## Educational Applications ### Student Projects - **Guided Experiments**: Step-by-step workflows for learning - **Template Library**: Pre-built experiments for common topics - **Customization**: Students can modify parameters and see results - **Documentation**: Automatic generation of lab reports ### Research Workflows - **Complex Analyses**: Multi-step research processes - **Reproducible Science**: Exact reproduction of published results - **Collaboration**: Share complex workflows with colleagues - **Publication**: Generate publication-ready figures and reports ### Course Development - **Lab Preparation**: Create standardized lab procedures - **Assessment**: Automated grading of student work - **Resource Management**: Efficient use of computing resources - **Quality Control**: Ensure consistent results across sections ## Advanced Features ### Complex DAG Example ```json { "tool": "experiment_orchestrator", "params": { "action": "define_dag", "name": "Particle Physics Analysis", "nodes": [ { "id": "import_data", "tool": "api_tools", "params": { "api": "cern", "dataset_name": "CMS-Run2011A-MuOnia" } }, { "id": "preprocess", "tool": "data", "params": { "action": "filter", "filter_type": "lowpass", "cutoff_freq": 1000 }, "dependencies": ["import_data"] }, { "id": "analyze_signal", "tool": "ml_ai_augmentation", "params": { "action": "pattern_recognition_infer", "model_type": "yolo" }, "dependencies": ["preprocess"] }, { "id": "visualize_results", "tool": "plot", "params": { "plot_type": "function_2d", "data": "from:analyze_signal.results" }, "dependencies": ["analyze_signal"] }, { "id": "generate_report", "tool": "export_tool", "params": { "export_type": "overleaf", "artifacts": "from:visualize_results" }, "dependencies": ["visualize_results"] } ] } } ``` ### Parameter Sweeps ```json { "tool": "experiment_orchestrator", "params": { "action": "define_dag", "name": "Parameter Study", "parameter_sweep": { "parameter": "frequency", "values": [1.0, 2.0, 3.0, 4.0, 5.0], "parallel": true }, "nodes": [ { "id": "calculate", "tool": "quantum", "params": { "problem": "sho", "frequency": "from:sweep" } } ] } } ``` ### Conditional Execution ```json { "tool": "experiment_orchestrator", "params": { "action": "define_dag", "name": "Conditional Analysis", "nodes": [ { "id": "check_data", "tool": "data", "params": { "action": "validate", "file": "input.csv" } }, { "id": "process_if_valid", "tool": "cas", "params": { "expr": "analyze_data()" }, "dependencies": ["check_data"], "condition": "check_data.valid == true" } ] } } ``` ## Execution Policies ### Local Execution - **Fast Development**: Run experiments on your local machine - **Interactive Debugging**: Step through experiments interactively - **Resource Control**: Full control over local resources - **Privacy**: Keep sensitive data on your machine ### Remote Execution - **HPC Clusters**: Use institutional supercomputers - **Cloud Computing**: Scale to cloud platforms - **Resource Sharing**: Efficient use of shared resources - **Cost Optimization**: Pay only for what you use ### Hybrid Execution - **Intelligent Scheduling**: Automatically choose best execution location - **Load Balancing**: Distribute work based on resource availability - **Fault Tolerance**: Handle failures gracefully - **Cost Awareness**: Balance performance and cost ## Quality Assurance ### Validation Pipeline - **Syntax Checking**: Verify DAG structure and syntax - **Parameter Validation**: Ensure all parameters are valid - **Resource Estimation**: Predict resource requirements - **Safety Checks**: Prevent dangerous or expensive operations ### Testing Framework - **Unit Tests**: Test individual nodes in isolation - **Integration Tests**: Test complete workflows - **Regression Tests**: Ensure changes don't break existing workflows - **Performance Tests**: Validate performance characteristics ### Monitoring and Logging - **Real-time Monitoring**: Track experiment progress - **Detailed Logging**: Complete audit trail of all operations - **Error Reporting**: Clear error messages and debugging information - **Performance Metrics**: Track resource usage and execution time ## Integration Examples ### Complete Research Pipeline ```json { "tool": "experiment_orchestrator", "params": { "action": "define_dag", "name": "Complete Research Workflow", "nodes": [ { "id": "literature_review", "tool": "api_tools", "params": { "api": "arxiv", "query": "quantum computing" } }, { "id": "data_collection", "tool": "data", "params": { "action": "import_hdf5", "file": "experiment_data.h5" } }, { "id": "analysis", "tool": "ml_ai_augmentation", "params": { "action": "symbolic_regression_train" }, "dependencies": ["data_collection"] }, { "id": "visualization", "tool": "plot", "params": { "plot_type": "function_2d" }, "dependencies": ["analysis"] }, { "id": "collaboration", "tool": "distributed_collaboration", "params": { "action": "session_share" }, "dependencies": ["visualization"] }, { "id": "publication", "tool": "export_tool", "params": { "export_type": "overleaf" }, "dependencies": ["collaboration"] } ] } } ``` ## Best Practices ### DAG Design - **Modularity**: Break complex experiments into smaller, reusable components - **Clarity**: Use descriptive names and clear documentation - **Efficiency**: Minimize dependencies to maximize parallelization - **Robustness**: Include error handling and validation steps ### Resource Management - **Resource Estimation**: Accurately estimate resource requirements - **Caching**: Use caching to avoid redundant computations - **Cleanup**: Clean up temporary files and resources - **Monitoring**: Monitor resource usage and adjust as needed ### Collaboration - **Documentation**: Document all experiments thoroughly - **Version Control**: Use version control for all DAG definitions - **Sharing**: Share successful workflows with the community - **Feedback**: Gather feedback and improve workflows iteratively