What types of problems can Gurddy solve?
Constraint Satisfaction Problems (CSP), Linear Programming (LP) and Mixed Integer Programming (MIP), and Minimax/game theory problems.
@novvoo
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
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"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"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。
Gurddy is an MCP (Model Context Protocol) server that solves Constraint Satisfaction Problems (CSP), Linear Programming (LP), and Minimax optimization problems using the gurddy library. It provides 13 MCP tools over stdio (for IDE integration) and HTTP/SSE transports (for web clients), and is intended for developers and AI assistants needing optimization capabilities.
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.
Constraint Satisfaction Problems (CSP), Linear Programming (LP) and Mixed Integer Programming (MIP), and Minimax/game theory problems.
Stdio (for local IDE integration) and HTTP/SSE (for remote web clients).
Install via `pip install gurddy_mcp`. For HTTP deployment, use the Docker image `gurddy-mcp`.
Yes, a live demo is hosted at https://gurddy-mcp.fly.dev (indicated by the badge in the README).
Yes, the server is a Python package and requires Python 3.8+ (the badge shows Python support).

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