MCP-ORTools

What is MCP-ORTools? MCP-ORTools is a server implementation of the Model Context Protocol (MCP) using Google OR-Tools for constraint solving. It enables large language models to interact with constraint models for efficient problem-solving. How to use MCP-ORTools? To use MCP-ORTools, install the package via pip, configure your application with a setup file, and define models in JSON format specifying variables, constraints, and optional objectives. Key features of MCP-ORTools? Integration with Google OR-Tools for constraint programming JSON-based model specification approach Comprehensive support for various optimization problems Compatibility with both integer and boolean variables Extensive constraint relationship definitions Use cases of MCP-ORTools? Optimizing supply chain logistics through integer programming. Solving scheduling problems within operations management. Assisting in combinatorial optimization tasks such as the knapsack problem. FAQ from MCP-ORTools? What types of problems can MCP-ORTools solve? MCP-ORTools can address a wide range of optimization and constraint satisfaction problems using linear and binary constraints. Is there support for different variable types? Yes, the implementation supports both integer and boolean variables. How can I define constraints in my models? Constraints should be defined using OR-Tools method syntax, including relational operators and methods for equality, inequality, and linear combinations.

As an MCP (Model Context Protocol) server, MCP-ORTools enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use MCP-ORTools, install the package via pip, configure your application with a setup file, and define models in JSON format specifying variables, constraints, and optional objectives. Key features of MCP-ORTools? Integration with Google OR-Tools for constraint programming JSON-based model specification approach Comprehensive support for various optimization problems Compatibility with both integer and boolean variables Extensive constraint relationship definitions Use cases of MCP-ORTools? Optimizing supply chain logistics through integer programming. Solving scheduling problems within operations management. Assisting in combinatorial optimization tasks such as the knapsack problem. FAQ from MCP-ORTools? What types of problems can MCP-ORTools solve? MCP-ORTools can address a wide range of optimization and constraint satisfaction problems using linear and binary constraints. Is there support for different variable types? Yes, the implementation supports both integer and boolean variables. How can I define constraints in my models? Constraints should be defined using OR-Tools method syntax, including relational operators and methods for equality, inequality, and linear combinations.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.