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MCP Constrained Optimization

@Sharmarajnish

Repository

Basic information

Category

Other

Transports

stdio

Author

Sharmarajnish

Submitted by

Rajnish Sharma

About MCP Constrained Optimization

No overview available yet

#mcp#constrained-optimization#optimization-server

MCP Constrained Optimization config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "MCP-Constrained-Optimization": {
      "command": "python",
      "args": [
        "examples/nqueens.py"
      ]
    }
  }
}

MCP Constrained Optimization tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

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.

Frequently asked questions

1

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.

2

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.

3

What problem types can Mcp_constrained_optimization solve?

The server handles linear programming, quadratic programming, convex optimization, constraint satisfaction problems, and portfolio optimization.

4

Is Mcp_constrained_optimization designed for AI assistants?

Yes, it is purpose-built for use with AI assistants through the Model Context Protocol (MCP).

5

What license is Mcp_constrained_optimization under?

It is licensed under the Apache License 2.0.

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