Skip to main content
Glama

Gurddy MCP Server

by novvoo
GitHub
Python
MIT License
OverviewInspectNewEndpointsSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

gurddy-mcp

This repository contains a fully functional MCP (Model Context Protocol) server, providing solutions for Constraint Satisfaction Problems (CSP) and Linear Programming (LP). It is based on the gurddy package and supports solving a variety of classic problems.

Main Features

CSP Problem Solving

  • N-Queens Problem: Place N queens on an N×N chessboard so that they do not attack each other

  • Graph Coloring Problem: Assign colors to graph vertices so that adjacent vertices have different colors

  • Map Coloring Problem: Assign colors to map regions so that adjacent regions have different colors

  • Sudoku Solving: Solve 9×9 Sudoku puzzles

  • General CSP Solver: Supports custom constraint satisfaction problems

LP/Optimization Problems

  • Linear Programming: Solve optimization problems with linear objective functions and constraints

  • Production Planning: Solve production optimization problems under resource constraints

  • Integer Programming: Supports optimization problems with integer variables

HTTP API Service

  • RESTful API interface, supporting JSON requests and responses

  • Comprehensive error handling and performance monitoring

  • Supports online solving of various CSP and LP problems

Quick Start

Environment Preparation

# Install dependencies pip install -r requirements.txt # Or manually install the main dependencies pip install fastapi uvicorn gurddy pulp requests

Usage

1. HTTP API Service

Start the HTTP server:

uvicorn mcp_server.http_api:app --host 127.0.0.1 --port 8000

Access the API documentation: http://127.0.0.1:8000/docs

2. Directly Calling the Demo

Run the complete CSP functionality demonstration:

python demo_csp_examples.py

Test the API functions (no server required):

python test_api_direct.py

3. Command Line Tools

Check/Install gurddy:

python -c "from mcp_server.tools.gurddy_install import run; print(run({'package':'gurddy'}))"

Run example:

python -c "from mcp_server.tools.gurddy_demo import run; print(run({'example':'csp'}))"

API endpoint

CSP problem solution

N-Queens problem

POST /solve-n-queens { "n": 8 }

Graph coloring problem

POST /solve-graph-coloring { "edges": [[0,1], [1,2], [2,0]], "num_vertices": 3, "max_colors": 3 }

Map coloring problem

POST /solve-map-coloring { "regions": ["A", "B", "C"], "adjacencies": [["A", "B"], ["B", "C"]], "max_colors": 2 }

Sudoku Solver

POST /solve-sudoku { "puzzle": [[5,3,0,...], [6,0,0,...], ...] }

General CSP Solver

POST /solve-csp { "problem_type": "n_queens", "parameters": {"n": 4} }

LP/Optimization Problems

Linear Programming

POST /solve-lp { "problem": { "profits": {"ProductA": 10, "ProductB": 15}, "consumption": { "ProductA": {"Resource1": 2, "Resource2": 1}, "ProductB": {"Resource1": 1, "Resource2": 3} }, "capacities": {"Resource1": 100, "Resource2": 80} } }

Production Planning

POST /solve-production-planning { "profits": {"ProductA": 10, "ProductB": 15}, "consumption": {...}, "capacities": {...}, "integer": true, "sensitivity_analysis": false }

Project Structure

mcp_server/ ├── handlers/ │ └── gurddy.py # Core solver implementation ├── tools/ # MCP tool wrapper ├── examples/ # Rich CSP Problem Examples │ ├── n_queens.py # N-Queens Problem │ ├── graph_coloring.py # Graph Coloring Problem │ ├── map_coloring.py # Map Coloring Problem │ ├── logic_puzzles.py # Logic Puzzles │ └── scheduling.py # Scheduling Problem ├── http_api.py # HTTP API Server └── server.py # MCP Server # Test and Demo Files demo_csp_examples.py # Full Functionality Demonstration test_api_direct.py # Direct API Test test_csp_api.py # HTTP API Test CSP_API_GUIDE.md # API Usage Guide ## Usage Examples ### Classic Problem Solving #### Coloring the Australian Map ```python import requests response = requests.post("http://127.0.0.1:8000/solve-map-coloring", json={ "regions": ['WA', 'NT', 'SA', 'QLD', 'NSW', 'VIC', 'TAS'], "adjacencies": [ ['WA', 'NT'], ['WA', 'SA'], ['NT', 'SA'], ['NT', 'QLD'], ['SA', 'QLD'], ['SA', 'NSW'], ['SA', 'VIC'], ['QLD', 'NSW'], ['NSW', 'VIC'] ], "max_colors": 4 })

8 Queen Problem

response= requests.post("http://127.0.0.1:8000/solve-n-queens", json={"n": 8})

Petersen Graph Coloring

response = requests.post("http://127.0.0.1:8000/solve-graph-coloring", json={ "edges": [ [0,1], [1,2], [2,3], [3,4], [4,0], # Outer loop [5,6], [6,7], [7,8], [8,9], [9,5], # Inner loop [0,5], [1,6], [2,7], [3,8], [4,9] # Connections ], "num_vertices": 10, "max_colors": 3 })

Supported Problem Types

CSP Problems

  • N-Queens: The classic N-Queens problem, supporting chessboards of any size

  • Graph Coloring: Vertex coloring of arbitrary graph structures

  • Map Coloring: Coloring geographic regions, verifying the Four Color Theorem

  • Sudoku: Solving standard 9×9 Sudoku puzzles

  • Logic Puzzles: Including classic logical reasoning problems such as the Zebra Puzzle

  • Scheduling: Course scheduling, meeting scheduling, resource allocation, etc.

Optimization Problems

  • Linear Programming: Linear optimization with continuous variables

  • Integer Programming: Optimization with discrete variables

  • Production Planning: Production optimization under resource constraints

  • Mixed Integer Programming: Optimization with a mix of continuous and discrete variables

Performance Features

  • Fast Solution: Typically completes in milliseconds for small to medium-sized problems (N-Queens with N ≤ 12, graph coloring with < 50 vertices)

  • Memory Efficient: Uses backtracking search and constraint propagation, resulting in a small memory footprint.

  • Extensible: Supports custom constraints and objective functions

  • Concurrency-Safe: The HTTP API supports concurrent request processing

Troubleshooting

Common Errors

  • "gurddy package not available": Need to install the gurddy package

  • "No solution found": No solution found under the given constraints; try relaxing the constraints.

  • "Invalid input types": Check the data types of the input parameters.

  • "Unknown problem type": Use a supported problem type.

Installation Issues

# If gurddy installation fails, try upgrading pip. pip install --upgrade pip pip install gurddy # If PuLP installation fails, pip install pulp # Check the Python environment. python -c "import sys; print(sys.executable)" ## Extension Development ### Adding a New CSP Problem 1. In `mcp_server/examples/` Create a problem implementation in `mcp_server/handlers/gurddy.py` 2. Add the solver function in `mcp_server/handlers/gurddy.py` 3. Add the API endpoint in `mcp_server/http_api.py` ### Custom Constraints ```python # Define a custom constraint in gurddy def custom_constraint(var1, var2): return var1 + var2 <= 10 model.addConstraint(gurddy.FunctionConstraint(custom_constraint, (var1, var2)))

License

This project is licensed under an open source license. Please see the LICENSE file for details.

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables 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.

  1. Main Features
    1. CSP Problem Solving
    2. LP/Optimization Problems
    3. HTTP API Service
  2. Quick Start
    1. Environment Preparation
  3. Usage
    1. 1. HTTP API Service
    2. 2. Directly Calling the Demo
    3. 3. Command Line Tools
  4. API endpoint
    1. CSP problem solution
    2. LP/Optimization Problems
  5. Project Structure
    1. 8 Queen Problem
    2. Petersen Graph Coloring
  6. Supported Problem Types
    1. CSP Problems
    2. Optimization Problems
  7. Performance Features
    1. Troubleshooting
      1. Common Errors
      2. Installation Issues
    2. License

      New MCP Servers

      MCP directory API

      We provide all the information about MCP servers via our MCP API.

      curl -X GET 'https://glama.ai/api/mcp/v1/servers/novvoo/gurddy-mcp'

      If you have feedback or need assistance with the MCP directory API, please join our Discord server