Gurddy
About
A Model Context Protocol (MCP) server providing solutions for Constraint Satisfaction Problems (CSP) and Linear Programming (LP). Built on the gurddy optimization library, it supports solving a variety of classic problems through two MCP transports: stdio (for IDE integration) an
Details
- Author
- novvoo
- Downloads
- 242
- Categories
- Developer Tools, Project Management, Automation
Jump to
- CSP solving (N‑Queens, Sudoku, graph/map coloring, scheduling)
- Linear and Mixed Integer Programming optimization
- Minimax and game theory (zero‑sum games, Nash equilibria)
- Robust optimization and conservative decision making
- Stdio and HTTP/SSE transport support
- 13 dedicated MCP tools for various problem types
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
GurddyCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install via pip install gurddy_mcp, then configure the MCP server in your IDE (e.g., Kiro) using a JSON block with command "uvx" and args ["gurddy-mcp@latest"]. Alternatively, run the HTTP server via Docker with docker run -p 8080:8080 gurddy-mcp. Example MCP configuration is provided in the README.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"gurddy": {
"gurddy": {
"command": "uvx",
"args": [
"gurddy-mcp@latest"
],
"env": [],
"disabled": false,
"autoApprove": [
"run_example",
"info",
"install",
"solve_n_queens",
"solve_sudoku",
"solve_graph_coloring",
"solve_map_coloring",
"solve_lp",
"solve_production_planning"
]
}
}
}
}McpServers
{
"gurddy": {
"command": "uvx",
"args": [
"gurddy-mcp@latest"
],
"env": [],
"disabled": false,
"autoApprove": [
"run_example",
"info",
"install",
"solve_n_queens",
"solve_sudoku",
"solve_graph_coloring",
"solve_map_coloring",
"solve_lp",
"solve_production_planning"
]
}
}Gurddy MCP Server
A comprehensive Model Context Protocol (MCP) server for solving Constraint Satisfaction Problems (CSP), Linear Programming (LP), and Minimax optimization problems. Built on the gurddy optimization library, it supports solving various classic problems through two MCP transports: stdio (for IDE integration) and HTTP/SSE (for web clients).
🚀 Quick Start (Stdio): pip install gurddy_mcp then configure in your IDE
🌐 Quick Start (HTTP): docker run -p 8080:8080 gurddy-mcp or see deployment guide
📦 PyPI Package: https://pypi.org/project/gurddy_mcp
Main Features
🎯 CSP Problem Solving
- N-Queens Problem: Place N queens on an N×N chessboard with no attacks - Graph Coloring: Assign colors to vertices so adjacent vertices differ - Map Coloring: Color geographic regions with adjacent regions differing - Sudoku Solver: Solve standard 9×9 Sudoku puzzles - Logic Puzzles: Einstein's Zebra puzzle and custom logic problems - Scheduling: Course scheduling, meeting scheduling, resource allocation - General CSP Solver: Support for custom constraint satisfaction problems📊 LP/Optimization Problems
- Linear Programming: Continuous variable optimization with linear constraints - Mixed Integer Programming: Optimization with integer and continuous variables - Production Planning: Resource-constrained production optimization with sensitivity analysis - Portfolio Optimization: Investment allocation under risk constraints - Transportation Problems: Supply chain and logistics optimization🎮 Minimax/Game Theory
- Zero-Sum Games: Solve two-player games (Rock-Paper-Scissors, Matching Pennies, Battle of Sexes) - Mixed Strategy Nash Equilibria: Find optimal probabilistic strategies - Robust Optimization: Minimize worst-case loss under uncertainty - Maximin Decisions: Maximize worst-case gain (conservative strategies) - Security Games: Defender-attacker resource allocation - Robust Portfolio: Minimize maximum loss across market scenarios - Production Planning: Conservative production decisions (maximize minimum profit) - Advertising Competition: Market share games and competitive strategies🔌 MCP Protocol Support
- Stdio Transport: Local IDE integration (Kiro, Claude Desktop, Cline, etc.) - HTTP/SSE Transport: Web clients and remote access - Unified Interface: Same tools across both transports - JSON-RPC 2.0: Full protocol compliance - Auto-approval: Configure trusted tools for seamless executionInstallation
From PyPI (Recommended)
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