General Purpose MCP Server for Constrained Optimization - AI agents for optimization tasks such as portfolio optimization, scheduling, and combinatorial problems
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<p align="center"> <img src=".github/logo.png" width="500px" alt="Constrained Optimization MCP Server"> </p> # Constrained Optimization MCP Server A general-purpose Model Context Protocol (MCP) server for solving combinatorial optimization problems with logical and numerical constraints. This server provides a unified interface to multiple optimization solvers, enabling AI assistants to solve complex optimization problems across various domains. ## 🚀 Features - **Unified Interface**: Single MCP server for multiple optimization backends - **AI-Ready**: Designed for use with AI assistants through MCP protocol - **Portfolio Focus**: Specialized tools for portfolio optimization and risk management - **Extensible**: Modular design for easy addition of new solvers - **High Performance**: Optimized for large-scale problems - **Robust**: Comprehensive error handling and validation ## 🛠️ Supported Solvers * [`Z3`](https://github.com/Z3Prover/z3) - SMT solver for constraint satisfaction problems * [`CVXPY`](https://www.cvxpy.org/) - Convex optimization solver * [`HiGHS`](https://highs.dev/) - Linear and mixed-integer programming solver * [`OR-Tools`](https://developers.google.com/optimization) - Constraint programming solver ## 📦 Installation ```bash # Install the package pip install constrained-opt-mcp # Or install from source git clone https://github.com/your-org/constrained-opt-mcp cd constrained-opt-mcp pip install -e . ``` ## 📐 Mathematical Foundations ### Optimization Theory The Constrained Optimization MCP Server implements solutions for various classes of optimization problems: #### **Linear Programming (LP)** $$\min_{x} c^T x \quad \text{subject to} \quad Ax \leq b, \quad x \geq 0$$ #### **Quadratic Programming (QP)** $$\min_{x} \frac{1}{2}x^T Q x + c^T x \quad \text{subject to} \quad Ax \leq b, \quad x \geq 0$$ #### **Convex Optimization** $$\min_{x} f(x) \quad \text{subject to} \quad g_i(x) \leq 0, \quad h_j(x) = 0$$ Where $f$ and $g_i$ are…
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