What are the system requirements?
Python 3.11+, `uv` package manager, and solver-specific dependencies (MiniZinc, PySAT, Z3, Clingo). Supports macOS, Windows, and Linux.
@szeider
基本信息
Model Context Protocol (MCP) server for constraint optimization and solving"
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-solver": {
"command": "uv",
"args": [
"venv"
]
}
}
}Remove all items from the model
Add new item at a specific index
Delete item at index
Replace item at index
Get current model content with numbered items
Solve the model (with timeout parameter)
MCP Solver is a Model Context Protocol server that exposes constraint solving, SAT, SMT, and ASP capabilities to Large Language Models. It integrates with MiniZinc, PySAT, Z3 Python, and Clingo, allowing AI models to interactively create, edit, and solve constraint models through the MCP interface.
Requires Python 3.11+, uv package manager, and solver-specific dependencies. Clone the repository, create a virtual environment, and install with uv pip install -e ".[all]" for all solvers. Run a mode-specific command (e.g., mcp-solver-mzn for MiniZinc) to start the MCP server. Alternatively, use the included test client with uv run run-test <mode> --problem <path/to/problem.md>.
Python 3.11+, `uv` package manager, and solver-specific dependencies (MiniZinc, PySAT, Z3, Clingo). Supports macOS, Windows, and Linux.
Five modes: MiniZinc (constraint programming), PySAT (SAT), MaxSAT (weighted optimization), Z3 (SMT), and ASP (logic programs with Clingo).
Clone repo, install with uv, then run the mode-specific command (e.g., `mcp-solver-mzn` for MiniZinc or `mcp-solver-pysat` for PySAT).
Six tools: `clear_model`, `add_item`, `delete_item`, `replace_item`, `get_model`, and `solve_model` (with timeout parameter).
Yes, the test client supports Anthropic, OpenAI, Google (Gemini), OpenRouter, and local models via LM Studio. Set the corresponding API key via environment variable.

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