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MCP ORTools

@Jacck

Repository

Basic information

Category

Other

Transports

stdio

Author

Jacck

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

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

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

1

What solver does MCP-ORTools use?

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

2

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).

3

What status values can the solver return?

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

4

What operations are supported in constraints?

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

5

What is the license?

MIT License.

Comments

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