Google OR-Tools server

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

Install the package:

pip install git+https://github.com/Jacck/mcp-ortools.git

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

security - not tested

license - permissive license

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

Related Resources

Reddit Discussion about this server

Related MCP Servers

mcp-solver
szeider
-
security
A
license
-
quality
A Model Context Protocol (MCP) server that exposes MiniZinc constraint solving capabilities to Large Language Models.
Last updated -
72
Python
MIT License
MCP-Logic
angrysky56
-
security
A
license
-
quality
MCP-Logic is a server that provides AI systems with automated reasoning capabilities, enabling logical theorem proving and model verification using Prover9/Mace4 through a clean MCP interface.
Last updated -
20
MIT License
MCP Starter Server
MatthewDailey
A
security
A
license
A
quality
This server provides a minimal template for creating AI assistant tools using the ModelContextProtocol, featuring a simple 'hello world' tool example and development setups for building custom MCP tools.
Last updated -
1
7
8
TypeScript
The Unlicense
Awesome MCP FastAPI
MR-GREEN1337
-
security
F
license
-
quality
A production-ready MCP server built with FastAPI, providing an enhanced tool registry for creating, managing, and documenting AI tools for Large Language Models (LLMs).
Last updated -
13
Python

View all related MCP servers

Google OR-Tools server

MCP-ORTools

Overview

Installation

Model Specification

Constraint Syntax

Usage Examples

Simple Optimization Model

Knapsack Problem

Features

Supported Operations in Constraints

Development

Model Response Format

License

Related Resources

Related MCP Servers

mcp-solver

MCP-Logic

MCP Starter Server

Awesome MCP FastAPI

Appeared in Searches

New MCP Servers

MCP directory API