MCP Solver

Overview

The MCP Solver is a purpose‑built Model Context Protocol server that bridges large language models with a suite of formal reasoning engines. By exposing constraint‑solving, SAT, SMT, and ASP capabilities through a unified MCP interface, it allows AI assistants to construct, modify, and solve complex logical models in an interactive manner. Developers can now ask a language model to generate a MiniZinc specification, tweak it on the fly, and retrieve solutions—all without leaving their conversational workflow.

At its core, the server hosts five distinct backends: MiniZinc, PySAT, MaxSAT, Z3 SMT, and Clingo ASP. Each backend is wrapped in a lightweight MCP toolset that provides CRUD operations on the underlying model (, , etc.) and a command that runs the chosen solver with an optional timeout. This design gives developers fine‑grained control over model construction while keeping the interface consistent across solvers. The ability to edit individual items by index means a language model can incrementally refine constraints, debug errors, or experiment with different formulations in real time.

Key capabilities include:

• Model manipulation: Create and edit constraint models programmatically, enabling dynamic scenario generation.
• Solver diversity: Access to multiple solving paradigms (constraint programming, SAT, SMT, ASP) from a single API.
• Optimization support: MaxSAT mode for objective‑driven problems, and MiniZinc’s built‑in optimization features.
• Timeout control: Prevent runaway solving with a user‑configurable time limit.
• Solution retrieval: Structured output that can be parsed back into the conversation, allowing AI assistants to explain results or suggest alternative solutions.

Typical use cases span scheduling and resource allocation in operations research, formal verification tasks that require SMT reasoning, logic puzzle generation for educational tools, and knowledge‑base inference with ASP. In each scenario the MCP Solver lets a conversational AI act as an interactive solver: it can ask for help, propose modifications, and instantly see the impact on solutions—all while keeping the underlying logic transparent to the developer.

Because MCP is designed for extensibility, adding new backends or custom solvers requires only a small wrapper around the existing tool interface. This makes the MCP Solver an ideal component in AI‑augmented development pipelines, where a language model can serve as both designer and solver of complex formal models.