minizinc-mcp
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
MiniZinc Constraint Solver MCP Server
A Model Context Protocol (MCP) server that provides constraint solving capabilities using MiniZinc. This server exposes a single powerful tool for solving constraint satisfaction and optimization problems.
Features
The server provides one core tool:
solve_constraint - General purpose constraint solver that accepts any MiniZinc model with optional data parameters, solver selection, and timeout configuration
Related MCP server: Google OR-Tools server
Usage Options
Option 1: Use Hosted Version (Recommended)
The easiest way to get started is using our free hosted version at:
https://minizinc-mcp.up.railway.app/sseOption 2: Self-Host with Docker
Build and run locally:
git clone <repository-url>
cd ccake
docker build -t minizinc-mcp .
docker run -p 8000:8000 minizinc-mcpOption 3: Local Development
Prerequisites:
Python 3.11+
MiniZinc 2.8+ (install from https://www.minizinc.org/software.html)
git clone https://github.com/r33drichards/minizinc-mcp
cd minizinc-mcp
pip install -r requirements.txt
python main.pyUsage with Claude
got to the connectors settings page
click Add Custom Connector button
for name use "minizinc mcp"
for url use:
https://minizinc-mcp.up.railway.app/sseExample Usage
Once configured, you can ask Claude to solve constraint problems:
"Solve the 4-Queens problem where 4 queens must be placed on a 4x4 chessboard so that no two queens attack each other"
"Find the optimal solution to a knapsack problem with items having weights [2,3,4,5] and values [3,4,5,6] and capacity 7"
"Solve this custom constraint: I need two variables x and y between 1 and 10 where x + y = 15 and x < y"
Response Format
The solve_constraint tool returns a SolveResult object containing:
solutions: List of solutions foundstatus: Solving status (SATISFIED, OPTIMAL, UNSATISFIABLE, etc.)solve_time: Time taken to solve in secondsnum_solutions: Number of solutions founderror: Error message if solving failed
Each solution contains:
variables: Dictionary of variable names to valuesobjective: Objective value for optimization problemsis_optimal: Whether the solution is optimal
License
MIT License - see LICENSE file for details.
Usage with Claude Code
claude mcp add minizinc -t sse https://minizinc-mcp.up.railway.app/ssethen to test run the command
claudein the input window prompt:
Solve the 4-Queens problem where 4 queens must be placed on a 4x4 chessboard so that no two queens attack each other
and you should see an output similar to this:

Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
This server cannot be deployed
Maintenance
Related MCP Connectors
Solve production, blending, transport and scheduling problems with LP and MIP.
Optimize crew and workforce schedules, resource allocation, and routing with linear and mixed-inte…
Manage SysML2 projects; check, evaluate, verify, query, document and diagram models.
FinOps for Google Cloud: size 1- and 3-year CUD commitments to known demand. Also solves LP/MIP.
Related MCP Servers
- AlicenseAqualityCmaintenanceA Model Context Protocol (MCP) server that exposes MiniZinc constraint solving capabilities to Large Language Models.6187MIT
- AlicenseNot gradedqualityDmaintenanceMCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution22MIT
- AlicenseNot gradedqualityDmaintenanceEnables solving Constraint Satisfaction Problems (CSP) like N-Queens, graph coloring, and Sudoku, as well as Linear Programming optimization problems through both MCP tools and HTTP API endpoints.2MIT
- AlicenseNot gradedqualityNot gradedmaintenanceAn MCP server that enables Large Language Models to interactively create, edit, and solve constraint models using backends like MiniZinc, Z3, PySAT, and Clingo. It bridges natural language with symbolic reasoning for solving complex logical, SAT, SMT, and optimization problems.MIT