MCP Solver MCP Server

What does “MCP Solver” MCP Server do?

The MCP Solver is a Model Context Protocol (MCP) server designed to provide advanced constraint optimization and solving functionalities to AI assistants and Large Language Models (LLMs). By integrating SAT (Boolean Satisfiability), SMT (Satisfiability Modulo Theories), and constraint solving capabilities, MCP Solver enables AI models to interactively create, modify, and solve complex mathematical models. It supports various problem representations, including MiniZinc for constraint models, PySAT for SAT and MaxSAT problems, and Z3 for SMT formulas. This empowers developers and AI agents to perform tasks such as automated reasoning, optimization, and model analysis, streamlining workflows in research, engineering, and decision-making applications. The server bridges the gap between advanced computational solvers and AI-driven interfaces, making it easier to leverage these tools in automated pipelines and interactive AI systems.

List of Prompts

• No explicit prompt templates were found in the repository files or documentation.
  (If any prompt templates are added in the future, they would be listed here.)

List of Resources

• No explicit MCP resources are described in the available documentation or files.
  (If the server exposes data/content resources in the future, they would be listed here.)

List of Tools

• clear_model: Remove all items from the current model.
• add_item: Add a new item at a specific index in the model.
• delete_item: Delete an item at a specified index from the model.
• replace_item: Replace an item at a specified index in the model.
• get_model: Retrieve the current content of the model with numbered items.
• solve_model: Solve the model, with support for a timeout parameter.

Use Cases of this MCP Server

• Constraint Model Development: Allows AI assistants to build and edit mathematical models using MiniZinc or PySAT, facilitating rapid prototyping and iterative refinement of constraints.
• Automated Problem Solving: Enables AI-driven workflows to automatically solve SAT, SMT, or optimization problems, providing solutions or identifying unsatisfiable constraints in real time.
• Optimization Tasks: Supports MaxSAT and MiniZinc optimization, making it possible for developers to find optimal solutions for resource allocation, scheduling, or combinatorial problems.
• Educational Tools: Integrates with teaching platforms or learning environments, allowing students to interactively explore constraint programming and logic solving via AI agents.
• Research Automation: Facilitates large-scale experimentation with constraint models, SAT instances, or SMT formulas, automating solver selection and result analysis through AI interfaces.

How to set it up

Windsurf

1. Prerequisites: Install Python 3.11+ and the uv project manager.
2. Clone and Install MCP Solver:

   git clone https://github.com/szeider/mcp-solver.git
   cd mcp-solver
   uv venv
   source .venv/bin/activate
   uv pip install -e ".[all]"
   

3. Locate Windsurf configuration file (typically windsurf.json or similar).
4. Add MCP Solver to mcpServers:

   {
     "mcpServers": {
       "mcp-solver": {
         "command": "python",
         "args": ["-m", "mcp_solver"]
       }
     }
   }
   

5. Save configuration and restart Windsurf.
6. Verify setup by testing tool access from the AI agent.

Securing API Keys (if needed)

{
  "mcpServers": {
    "mcp-solver": {
      "command": "python",
      "args": ["-m", "mcp_solver"],
      "env": {
        "SOLVER_API_KEY": "${SOLVER_API_KEY}"
      },
      "inputs": {
        "api_key": "${SOLVER_API_KEY}"
      }
    }
  }
}

Claude

1. Ensure Python 3.11+ and uv are installed.
2. Install MCP Solver as above.
3. Find Claude’s configuration file and open it.
4. Add MCP Solver server configuration:

   {
     "mcpServers": {
       "mcp-solver": {
         "command": "python",
         "args": ["-m", "mcp_solver"]
       }
     }
   }
   

5. Restart Claude and check for MCP tool availability.

Cursor

1. Install Python 3.11+ and uv.
2. Download and install MCP Solver as in the quick start.
3. Edit Cursor’s config file (e.g., cursor.json).
4. Add MCP Solver:

   {
     "mcpServers": {
       "mcp-solver": {
         "command": "python",
         "args": ["-m", "mcp_solver"]
       }
     }
   }
   

5. Restart Cursor to apply changes.

Cline

1. Set up Python 3.11+ and uv.
2. Clone and install MCP Solver.
3. Open Cline’s configuration file.
4. Append the MCP Solver server entry:

   {
     "mcpServers": {
       "mcp-solver": {
         "command": "python",
         "args": ["-m", "mcp_solver"]
       }
     }
   }
   

5. Save, restart Cline, and confirm tool access.

Note: If your setup requires API keys or secrets, use environment variables as in the Windsurf example above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "mcp-solver": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “mcp-solver” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

| Roots Support | ⛔ | Not mentioned |

Based on the available documentation, MCP Solver is a robust and specialized MCP server focused on constraint and optimization problem solving, providing well-defined tools but lacking explicit prompt templates and resources. It is well-documented for setup and integration but does not mention support for advanced MCP features like roots or sampling.

MCP Score

Our opinion:
The MCP Solver is a highly focused, academically robust MCP server with strong solver integration and tool support. Its lack of prompt templates and explicit resources limits its generality, but its core functionality for constraint/optimization workflows is excellent. It would rate a 7/10 for general-purpose MCP evaluation—higher if prompt/resource support is added.