Mcp_constrained_optimization
@Sharmarajnish
About Mcp_constrained_optimization
No overview available yet
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MCP-Constrained-Optimization": {
"command": "python",
"args": [
"examples/nqueens.py"
]
}
}
}Tools
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Overview
What is Mcp_constrained_optimization?
Mcp_constrained_optimization is a general-purpose MCP server for solving combinatorial optimization problems with logical and numerical constraints. It provides a unified interface to multiple optimization solvers, enabling AI assistants to handle complex optimization across various domains.
How to use Mcp_constrained_optimization?
Install via pip install constrained-opt-mcp, then start the server with constrained-opt-mcp. Add it to your MCP client configuration using the command constrained-opt-mcp with no arguments. The server exposes five tools: solve_constraint_satisfaction, solve_convex_optimization, solve_linear_programming, solve_constraint_programming, and solve_portfolio_optimization.
Key features of Mcp_constrained_optimization
- Unified interface to Z3, CVXPY, HiGHS, and OR-Tools solvers.
- Specialized tools for portfolio optimization and risk management.
- Designed for AI assistants via the MCP protocol.
- Supports linear, quadratic, convex, and constraint satisfaction problems.
- High performance with comprehensive error handling.
- Extensible modular architecture for adding new solvers.
Use cases of Mcp_constrained_optimization
- Solve constraint satisfaction puzzles like N‑Queens and knapsack problems.
- Perform portfolio optimization with Markowitz, Black‑Litterman, or ESG constraints.
- Optimize production planning and resource allocation via linear programming.
- Schedule workforce, job shop, or nurse shifts with constraint programming.
- Manage risk using VaR, CVaR, stress testing, and scenario analysis.
FAQ from Mcp_constrained_optimization
What solvers does Mcp_constrained_optimization support?
It supports Z3 for constraint satisfaction, CVXPY for convex optimization, HiGHS for linear/mixed-integer programming, and OR-Tools for constraint programming.
How do I install Mcp_constrained_optimization?
Install via pip: pip install constrained-opt-mcp. You can also clone the repository and run pip install -e . from the source directory.
What problem types can Mcp_constrained_optimization solve?
The server handles linear programming, quadratic programming, convex optimization, constraint satisfaction problems, and portfolio optimization.
Is Mcp_constrained_optimization designed for AI assistants?
Yes, it is purpose-built for use with AI assistants through the Model Context Protocol (MCP).
What license is Mcp_constrained_optimization under?
It is licensed under the Apache License 2.0.
Frequently asked questions
What solvers does Mcp_constrained_optimization support?
It supports Z3 for constraint satisfaction, CVXPY for convex optimization, HiGHS for linear/mixed-integer programming, and OR-Tools for constraint programming.
How do I install Mcp_constrained_optimization?
Install via pip: `pip install constrained-opt-mcp`. You can also clone the repository and run `pip install -e .` from the source directory.
What problem types can Mcp_constrained_optimization solve?
The server handles linear programming, quadratic programming, convex optimization, constraint satisfaction problems, and portfolio optimization.
Is Mcp_constrained_optimization designed for AI assistants?
Yes, it is purpose-built for use with AI assistants through the Model Context Protocol (MCP).
What license is Mcp_constrained_optimization under?
It is licensed under the Apache License 2.0.
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