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Google OR-Tools server

by Jacck
GitHub
Python
MIT License
4
  • Apple
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MCP-ORTools

A Model Context Protocol (MCP) server implementation using Google OR-Tools for constraint solving. Designed for use with Large Language Models through standardized constraint model specification.

Overview

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the Model Context Protocol, enabling AI models to:

  • Submit and validate constraint models
  • Set model parameters
  • Solve constraint satisfaction and optimization problems
  • Retrieve and analyze solutions

Installation

  1. Install the package:
pip install git+https://github.com/Jacck/mcp-ortools.git
  1. Configure Claude Desktop Create the configuration file at %APPDATA%\Claude\claude_desktop_config.json (Windows) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):
{ "mcpServers": { "ortools": { "command": "python", "args": ["-m", "mcp_ortools.server"] } } }

Model Specification

Models are specified in JSON format with three main sections:

  • variables: Define variables and their domains
  • constraints: List of constraints using OR-Tools methods
  • objective: Optional optimization objective

Constraint Syntax

Constraints must use OR-Tools method syntax:

  • .__le__() for less than or equal (<=)
  • .__ge__() for greater than or equal (>=)
  • .__eq__() for equality (==)
  • .__ne__() for not equal (!=)

Usage Examples

Simple Optimization Model

{ "variables": [ {"name": "x", "domain": [0, 10]}, {"name": "y", "domain": [0, 10]} ], "constraints": [ "(x + y).__le__(15)", "x.__ge__(2 * y)" ], "objective": { "expression": "40 * x + 100 * y", "maximize": true } }

Knapsack Problem

Example: Select items with values [3,1,2,1] and weights [2,2,1,1] with total weight limit of 2.

{ "variables": [ {"name": "p0", "domain": [0, 1]}, {"name": "p1", "domain": [0, 1]}, {"name": "p2", "domain": [0, 1]}, {"name": "p3", "domain": [0, 1]} ], "constraints": [ "(2*p0 + 2*p1 + p2 + p3).__le__(2)" ], "objective": { "expression": "3*p0 + p1 + 2*p2 + p3", "maximize": true } }

Additional constraints example:

{ "constraints": [ "p0.__eq__(1)", // Item p0 must be selected "p1.__ne__(p2)", // Can't select both p1 and p2 "(p2 + p3).__ge__(1)" // Must select at least one of p2 or p3 ] }

Features

  • Full OR-Tools CP-SAT solver support
  • JSON-based model specification
  • Support for:
    • Integer and boolean variables (domain: [min, max])
    • Linear constraints using OR-Tools method syntax
    • Linear optimization objectives
    • Timeouts and solver parameters
    • Binary constraints and relationships
    • Portfolio selection problems
    • Knapsack problems

Supported Operations in Constraints

  • Basic arithmetic: +, -, *
  • Comparisons: .le(), .ge(), .eq(), .ne()
  • Linear combinations of variables
  • Binary logic through combinations of constraints

Development

To setup for development:

git clone https://github.com/Jacck/mcp-ortools.git cd mcp-ortools pip install -e .

Model Response Format

The solver returns solutions in JSON format:

{ "status": "OPTIMAL", "solve_time": 0.045, "variables": { "p0": 0, "p1": 0, "p2": 1, "p3": 1 }, "objective_value": 3.0 }

Status values:

  • OPTIMAL: Found optimal solution
  • FEASIBLE: Found feasible solution
  • INFEASIBLE: No solution exists
  • UNKNOWN: Could not determine solution

License

MIT License - see LICENSE file for details

This server cannot be installed

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security - not tested
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license - permissive license
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quality - not tested

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to:

Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

  1. Overview
    1. Installation
      1. Model Specification
        1. Constraint Syntax
      2. Usage Examples
        1. Simple Optimization Model
        2. Knapsack Problem
      3. Features
        1. Supported Operations in Constraints
      4. Development
        1. Model Response Format
          1. License

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