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MCP ORTools

@MCP-Reasoner

代码仓库

基本信息

分类

其他

许可证

MIT license

传输方式

stdio

作者

MCP-Reasoner

关于 MCP ORTools

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

#mcp-ortools#constraint-solving#ai-integration

MCP ORTools 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP ORTools 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP-ORTools?

MCP-ORTools is a Model Context Protocol server that integrates Google OR-Tools constraint programming solver with Large Language Models. It enables AI models to submit, validate, and solve constraint satisfaction and optimization problems using JSON-based model specifications.

How to use MCP-ORTools?

Install the package via pip install git+https://github.com/Jacck/mcp-ortools.git, then configure Claude Desktop by adding an ortools MCP server entry pointing to python -m mcp_ortools.server in the MCP configuration file. Submit constraint models in JSON format with variables, constraints, and optional objective sections.

Key features of MCP-ORTools

  • Full OR-Tools CP-SAT solver support
  • JSON-based model specification
  • Integer and boolean variable domains
  • Linear constraints using OR-Tools method syntax
  • Linear optimization objectives with maximize/minimize
  • Support for timeouts and solver parameters

Use cases of MCP-ORTools

  • Solving knapsack problems with variable selection constraints
  • Modeling and optimizing portfolio selection
  • Enforcing binary relationships between decision variables
  • Finding feasible solutions for linear constraint satisfaction problems
  • Setting optimal objective values within given constraints

常见问题

1

What constraint syntax is supported?

Constraints must use OR-Tools method syntax: `.__le__()` for ≤, `.__ge__()` for ≥, `.__eq__()` for equality, `.__ne__()` for not equal. Basic arithmetic (+, -, *) on variables is allowed.

2

What solver statuses can the server return?

The solver returns OPTIMAL, FEASIBLE, INFEASIBLE, or UNKNOWN along with solve time and variable assignments.

3

What are the runtime dependencies?

The server requires Python and the `mcp-ortools` package, which includes Google OR-Tools. No additional runtime or database is needed.

4

How are models validated?

Models are submitted as JSON with required `variables` and `constraints` arrays and an optional `objective` object. The server validates the syntax before solving.

5

What transport or authentication does the server use?

The README configures the server as an MCP server for Claude Desktop via stdio transport. No authentication details are provided.

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