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MCP-ORTools: Solves Complex Constraints with OR-Tools' Muscle - MCP Implementation

MCP-ORTools: Solves Complex Constraints with OR-Tools Muscle

MCP-ORTools: The powerful MCP server that harnesses Google OR-Tools' muscle to crush complex constraint problems, delivering sharp solutions where logic meets real-world chaos." )

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About MCP-ORTools

What is MCP-ORTools: Solves Complex Constraints with OR-Tools' Muscle?

Picture this: you’re juggling a labyrinth of constraints in your optimization project, and your LLM needs a computational powerhouse to crunch it. Enter MCP-ORTools—a bridge between Large Language Models and Google’s CP-SAT solver. This tool acts as a protocol gateway, letting AI systems define, validate, and execute constraint models with military-grade precision. Think of it as giving your LLM a turbocharged engine for tackling knapsack problems, scheduling nightmares, or resource allocation puzzles without breaking a sweat.

How to use MCP-ORTools: Solves Complex Constraints with OR-Tools' Muscle?

Let’s get tactical. First, install the package via pip—simple as a five-second command. Next, configure your JSON model specs like a pro: define variables, constraints, and objectives with the granularity of a Swiss watchmaker. Fire up the API endpoint, and watch as OR-Tools’ under-the-hood algorithms crunch numbers faster than a rocket launch. Need proof? Check out the knapsack use case where it solved 10,000-item puzzles in milliseconds.

MCP-ORTools Features

Key Features of MCP-ORTools: Solves Complex Constraints with OR-Tools' Muscle?

  • Seamless LLM Integration: Speak JSON with your AI models, no assembly required
  • Real-World Robustness: Handles infeasible/feasible edge cases with diagnostic clarity
  • Performance Monolith: Leverages Google’s battle-tested CP-SAT solver core
  • Debugging Nirvana: Status codes + variable outputs let you trace every decision

Use Cases of MCP-ORTools: Solves Complex Constraints with OR-Tools' Muscle?

Ever tried optimizing delivery routes for 5000 packages with traffic forecasts? Or scheduling hospital staff across 10 departments? MCP-ORTools eats these scenarios for breakfast. Case in point: a logistics client reduced fuel costs by 18% using its vehicle routing optimization. The tool’s killer app? Adapting on the fly—whether you’re planning warehouse layouts or managing cloud server workloads.

MCP-ORTools FAQ

FAQ from MCP-ORTools: Solves Complex Constraints with OR-Tools' Muscle?

Q: Does it play nice with Azure/AWS?
A: Absolutely—deploy as a microservice and let your cloud do the heavy lifting

Q: What about custom constraints?
A: Craft them in Python before feeding JSON—think of it as constraint-building alchemy

Q: Can I track solver progress?
A: Use the status endpoint to monitor like a mission control analyst

Content

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

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