MCP-ORTools MCP Server: AI Optimization and Constraint Solving

Overview: What is MCP-ORTools MCP Server?

MCP-ORTools is an advanced Model Context Protocol (MCP) server that integrates Google's OR-Tools constraint programming solver with Large Language Models through standardized model specification. Designed for complex AI workflows, it enables applications to submit and validate constraint models, set parameters, solve optimization and satisfaction problems, and retrieve analysis solutions efficiently.

🔧 Core Features & MCP Capabilities

MCP-ORTools offers a robust set of features that enhance the capabilities of Large Language Models in solving real-world problems. Key functionalities include:

• Full OR-Tools Support: Comprehensive integration with Google's CP-SAT solver for handling integer and boolean variables.
• JSON-Based Model Specification: AI applications can define models using standardized JSON formats, ensuring flexibility and ease of use.
• Model Parameters and Timeouts: Users can configure various solving parameters and set timeouts to optimize performance.

⚙️ MCP Architecture & Protocol Implementation

MCP-ORTools leverages the power of MCP by adhering to its protocol design. This ensures seamless communication between the AI application (such as Claude Desktop) and the server, facilitating the submission and retrieval of model definitions, constraints, objectives, and solutions in a structured manner.

MCP Protocol Flow Diagram

graph TD
    Substeps[AI Application] -->|MCP Client Request| Step1[MCP Protocol]
    Step1 -->|Model Specification & Parameters| Step2[MCP Server]
    Step2 -->|Constraints & Optimization| Step3[OR-Tools Solver]
    Step3 -->|Solution Data| Step4[MCP Server Response to MCP Client]
    Step4 -->|Data Transfer| Substeps

MCP Client Compatibility Matrix

MCP-ORTools supports multiple AI applications through its compatibility matrix. This ensures that developers can seamlessly integrate the server with various tools and environments.

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🚀 Getting Started with Installation

To set up MCP-ORTools, follow these steps:

1. Install the Package:

pip install git+https://github.com/Jacck/mcp-ortools.git

2. Configure Claude Desktop: Create a configuration file for your AI application at the appropriate path.

   • Windows:

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

   • macOS:

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

💡 Key Use Cases in AI Workflows

Simple Optimization Model Application

Consider a scenario where an e-commerce platform needs to optimize product pricing and inventory levels. By defining variables such as price and stock, and setting constraints on profit margins, the server can help find the optimal cost structure that maximizes revenue.

{
    "variables": [
        {"name": "x", "domain": [0, 10]},
        {"name": "y", "domain": [0, 10]}
    ],
    "constraints": [
        "(x + y).__le__(15)",
        "x.__ge__(2 * y)"
    ],
    "objective": {
        "expression": "40 * x + 100 * y",
        "maximize": true
    }
}

Knapsack Problem Solution

MCP-ORTools can also be utilized to solve complex problems like the knapsack problem. This is particularly useful in scenarios such as inventory management or resource allocation.

{
    "variables": [
        {"name": "p0", "domain": [0, 1]},
        {"name": "p1", "domain": [0, 1]},
        {"name": "p2", "domain": [0, 1]},
        {"name": "p3", "domain": [0, 1]}
    ],
    "constraints": [
        "(2*p0 + 2*p1 + p2 + p3).__le__(2)"
    ],
    "objective": {
        "expression": "3*p0 + p1 + 2*p2 + p3",
        "maximize": true
    }
}

🔌 Integration with MCP Clients

MCP-ORTools seamlessly integrates with popular MCP clients, making it easy for developers to extend the functionality of their AI applications. By configuring the server within these clients, users can leverage advanced constraint solving capabilities.

Sample Configuration Code

{
  "mcpServers": {
    "ortools": {
      "command": "python",
      "args": ["-m", "mcp_ortools.server"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

📊 Performance & Compatibility Matrix

MCP-ORTools ensures compatibility with a wide range of AI workflows and applications. The performance of the server is optimized for real-time solving, making it suitable for both development environments and production systems.

Application	Integration Level	API Support	Solver Efficiency
Claude Desktop	High	Full	Optimized

🛠️ Advanced Configuration & Security

For advanced users, MCP-ORTools offers enhanced configuration options and security settings. Developers can customize the server to suit their application's needs.

Examples of Advanced Configuration

{
  "mcpServers": {
    "ortools": {
      "command": "python",
      "args": ["-m", "mcp_ortools.server"],
      "env": {
        "API_KEY": "your-api-key",
        "TIMEOUT": "120s"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

Q1: Can MCP-ORTools integrate with multiple Large Language Models?

Yes, MCP-ORTools can be configured to work with various AI applications, ensuring compatibility across different environments and models.

Q2: How does the server handle complex constraint problems?

The OR-Tools solver is designed to solve complex constraints efficiently. Users can define intricate models, and the server will optimize for solutions with minimal computational overhead.

Q3: Are there any limitations when defining variables or constraints?

MCP-ORTools supports a wide range of variable domains and constraint types. Developers should refer to the OR-Tools documentation for best practices in defining complex models.

Q4: Can I customize solver parameters for specific tasks?

Certainly! Users can set various parameters such as timeouts, solver options, and API keys within the configuration file to tailor the server's behavior.

Q5: What kind of data is returned after solving a model?

The solution data includes the status of the problem (OPTIMAL, FEASIBLE, INFEASIBLE), solve time, variables' values, and objective function evaluations. This provides comprehensive feedback for further analysis.

👨‍💻 Development & Contribution Guidelines

Contributors can enhance the MCP-ORTools project by submitting pull requests or reporting issues according to the contributing guidelines found in the repository. Detailed instructions on setting up a development environment are provided.

Sample Pull Request

To contribute, developers should:

1. Fork the repository.
2. Create a new branch for feature implementation or bug fixes.
3. Write comprehensive tests and code documentation.
4. Submit a pull request detailing the changes made.

🌐 MCP Ecosystem & Resources

For further information on integrating with the MCP ecosystem, explore the following resources:

• MCP Documentation: Comprehensive guides and tutorials.
• OR-Tools Documentation: Detailed information about using OR-Tools solver methods.
• GitHub Repository: Latest code updates and community contributions.

By harnessing the power of MCP-ORTools, developers can unlock new possibilities in AI optimization and constraint solving.