MCP ORTools

@Jacck

Repository

Basic information

About MCP ORTools

Model Context Protocol (MCP) server implementation using Google OR-Tools for constraint solving

#mcp-ortools#constraint-solving#google-or-tools

MCP ORTools config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "ortools": {
      "command": "python",
      "args": [
        "-m",
        "mcp_ortools.server"
      ]
    }
  }
}

MCP ORTools tools

Tool list not available yet

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is 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.

How to use MCP-ORTools?

Install the package via pip install git+https://github.com/Jacck/mcp-ortools.git, then configure it as an MCP server in Claude Desktop by adding the server entry to claude_desktop_config.json with command python -m mcp_ortools.server. Models are submitted in JSON format with variables, constraints, and an optional objective.

Key features of MCP-ORTools

Integrates Google OR-Tools CP-SAT solver
JSON-based model specification
Supports integer and boolean variables
Linear constraints using OR-Tools method syntax
Linear optimization objectives and solver parameters

Use cases of MCP-ORTools

Solving knapsack and portfolio selection problems
Optimizing linear objectives with constraints
Enabling AI models to solve constraint satisfaction problems
Modeling binary logic and variable relationships

Frequently asked questions

What solver does MCP-ORTools use?

It uses Google OR-Tools CP‑SAT solver for constraint satisfaction and optimization.

How are models specified?

Models are specified in JSON with three sections: `variables` (name and domain), `constraints` (OR‑Tools method syntax), and an optional `objective` (linear expression and maximize/minimize flag).

What status values can the solver return?

The solver returns OPTIMAL, FEASIBLE, INFEASIBLE, or UNKNOWN.

What operations are supported in constraints?

Basic arithmetic (+, -, *) and comparisons via `.__le__()`, `.__ge__()`, `.__eq__()`, `.__ne__()`.

What is the license?

MIT License.

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