comp-model-mcp-server

# Physics Model MCP Server A modular platform for defining, generating, executing, and tracking physics model experiments with reproducibility. ## 🚀 Quick Start 1. **Clone & install dependencies** ```bash git clone https://github.com/your-org/comp-model-mcp-server.git cd comp-model-mcp-server pip install -r requirements.txt ``` 2. **Set environment variables** ```bash export OPENAI_API_KEY="your-openai-api-key" export DB_PATH="mcp_server.db" export GENERATED_MODELS_DIR="generated_models" export SANDBOX_TIMEOUT=30 ``` 3. **Run the server** ```bash uvicorn main_server:app --reload ``` --- ## 🔁 Reproduce an Experiment ### 1. Create a Model Submit a model specification: ```bash curl -X POST http://localhost:8000/models \ -H "Content-Type: application/json" \ -d '{ "model_name": "lorenz", "equations": ["dx/dt = sigma*(y - x)", "dy/dt = x*(rho - z) - y", "dz/dt = x*y - beta*z"], "parameters": [ {"name":"sigma","description":"Prandtl number","value":"10.0"}, {"name":"rho","description":"Rayleigh number","value":"28.0"}, {"name":"beta","description":"Geometric factor","value":"8.0/3.0"} ], "initial_conditions":[ {"name":"x0","description":"initial x","value":"1.0"}, {"name":"y0","description":"initial y","value":"1.0"}, {"name":"z0","description":"initial z","value":"1.0"} ] }' ``` Save the returned `model_id`. ### 2. Approve the Model ```bash curl -X POST http://localhost:8000/models/{model_id}/approve ``` ### 3. Run a Single Experiment ```bash curl -X 'POST' \ 'http://127.0.0.1:8000/models/lorenz_1ac9a383/run' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "parameters": [ { "name": "sigma", "value": 10.0 }, { "name": "rho", "value": 28.0 }, { "name": "beta", "value": 2.6666666666666665 } ], "initial_conditions": [ { "name": "x0", "value": 1.0 }, { "name": "y0", "value": 1.0 }, { "name": "z0", "value": 1.0 } ] } ' ``` > Response includes experiment `results`. ### 4. Retrieve Experiment Results ```bash curl http://localhost:8000/models/{model_id}/results ``` --- ## 🧬 CLI-Based Experiment Reproduction 1. **Fetch script** ```bash curl http://localhost:8000/models/{model_id}/code -o model.py ``` 2. **Run the script locally** ```bash python model.py --params '{ "params":{"sigma":10.0,"rho":28.0,"beta":2.6667}, "initial_conditions":{"x0":1.0,"y0":1.0,"z0":1.0} }' ``` The output will match the experiment results. --- ## 🛠️ Cleanup (Optional) Delete the model and all associated experiments: ```bash curl -X DELETE http://localhost:8000/models/{model_id} ``` --- ## 🤖 Embeddings & Analytics Stored models and experiments include: - **Text embeddings** of model specification - **Numeric + text joint embeddings** of experiment parameters/results These can be used for clustering, similarity search, or advanced analytics. --- ## 📦 Requirements - Python 3.10+ - Dependencies in `requirements.txt`: - `fastapi`, `uvicorn`, `openai`, `sentence-transformers`, `torch`, `numpy`, `scipy`, `sympy`, etc. To ensure reproducibility, use the same environment setup and input parameters.