Opti-MCP: Optimization MCP Server

A Model Context Protocol (MCP) server that provides tools for solving optimization problems using Google OR-Tools. This server enables AI assistants to solve Linear Programming (LP), Integer Programming (IP), Mixed-Integer Programming (MIP), and classic combinatorial optimization problems.

Features

• Linear Programming: Solve continuous optimization problems with linear constraints

• Integer/Mixed-Integer Programming: Solve optimization problems with integer decision variables

• Knapsack Problem: Solve the classic 0/1 knapsack problem efficiently

• Built on Google OR-Tools for robust, production-grade optimization

• Simple JSON-based interface

Related MCP server: MCP Optimizer

Prerequisites

• Node.js 18 or higher

• Python 3.8 or higher

• Google OR-Tools Python library

Installation

1. Clone this repository:

git clone <your-repo-url>
cd opti-mcp

2. Install Node.js dependencies:

npm install

3. Install Python dependencies:

pip install ortools

4. Build the TypeScript code:

npm run build

Configuration

Add to your MCP settings file (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "opti-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/opti-mcp/dist/index.js"]
    }
  }
}

Available Tools

1. solve_linear_program

Solves linear programming problems where all variables are continuous.

Example: Production Planning

{
  "objective": {
    "coefficients": [3, 5],
    "maximize": true
  },
  "constraints": [
    {
      "coefficients": [1, 0],
      "upper_bound": 4
    },
    {
      "coefficients": [0, 2],
      "upper_bound": 12
    },
    {
      "coefficients": [3, 2],
      "upper_bound": 18
    }
  ],
  "variable_bounds": [[0, "Infinity"], [0, "Infinity"]]
}

This maximizes 3x₀ + 5x₁ subject to:

• x₀ ≤ 4

• 2x₁ ≤ 12

• 3x₀ + 2x₁ ≤ 18

• x₀, x₁ ≥ 0

2. solve_integer_program

Solves integer or mixed-integer programming problems.

Example: Assignment Problem

{
  "objective": {
    "coefficients": [1, 2, 3, 4],
    "maximize": false
  },
  "constraints": [
    {
      "coefficients": [1, 1, 0, 0],
      "lower_bound": 1,
      "upper_bound": 1
    },
    {
      "coefficients": [0, 0, 1, 1],
      "lower_bound": 1,
      "upper_bound": 1
    }
  ],
  "variable_bounds": [[0, 1], [0, 1], [0, 1], [0, 1]],
  "integer_variables": [0, 1, 2, 3]
}

This solves an assignment problem where variables must be binary (0 or 1).

3. solve_knapsack

Solves the 0/1 knapsack problem.

Example: Item Selection

{
  "values": [360, 83, 59, 130, 431, 67, 230, 52, 93, 125],
  "weights": [7, 0, 30, 22, 80, 94, 11, 81, 70, 64],
  "capacity": 850
}

Maximizes total value while keeping total weight ≤ capacity.

Example Problems

Diet Problem

Minimize cost while meeting nutritional requirements:

{
  "objective": {
    "coefficients": [2.5, 1.8, 3.0, 0.5],
    "maximize": false
  },
  "constraints": [
    {
      "coefficients": [10, 5, 8, 2],
      "lower_bound": 50
    },
    {
      "coefficients": [3, 8, 1, 6],
      "lower_bound": 30
    },
    {
      "coefficients": [5, 4, 7, 3],
      "lower_bound": 40
    }
  ]
}

Bin Packing

Pack items into minimum number of bins:

{
  "objective": {
    "coefficients": [1, 1, 1],
    "maximize": false
  },
  "constraints": [
    {
      "coefficients": [5, 0, 0],
      "upper_bound": 10
    },
    {
      "coefficients": [0, 7, 0],
      "upper_bound": 10
    },
    {
      "coefficients": [0, 0, 4],
      "upper_bound": 10
    }
  ],
  "integer_variables": [0, 1, 2]
}

Response Format

All solvers return a JSON response with:

{
  "status": "OPTIMAL",
  "objective_value": 34.0,
  "solution": [2.0, 6.0],
  "solve_time_ms": 15
}

• status: OPTIMAL, INFEASIBLE, UNBOUNDED, or UNKNOWN

• objective_value: The optimal value found (null if not optimal)

• solution: Array of variable values (null if not optimal)

• solve_time_ms: Time taken to solve in milliseconds

For knapsack problems:

{
  "status": "OPTIMAL",
  "total_value": 1030,
  "total_weight": 850,
  "selected_items": [0, 2, 3, 4],
  "capacity": 850
}

Development

# Watch mode for development
npm run dev

# Build for production
npm run build

# Run the server
npm start

How It Works

The MCP server:

1. Receives optimization problems via MCP tool calls

2. Translates them into Python scripts using OR-Tools

3. Executes the Python scripts

4. Returns formatted results

Limitations

• Requires Python 3.8+ with OR-Tools installed

• Currently uses GLOP for LP and SCIP for MIP (requires SCIP installation for best performance)

• Large problems may take significant time to solve

License

MIT

Contributing

Contributions welcome! Please open an issue or PR.