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:
- 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):
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
Knapsack Problem
Example: Select items with values [3,1,2,1] and weights [2,2,1,1] with total weight limit of 2.
Additional constraints example:
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:
Model Response Format
The solver returns solutions in JSON format:
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