MCP‑ORTools: AI‑Powered Constraint Solving with Google OR‑Tools

MCP‑ORTools bridges the gap between large language models and sophisticated constraint programming by exposing Google OR‑Tools’ CP‑SAT solver through the Model Context Protocol. Developers can now let an AI assistant compose, validate, and solve complex scheduling, allocation, or optimization problems without leaving the conversational interface. The server accepts a lightweight JSON model that describes variables, linear constraints, and optional objectives, then returns the solution as structured JSON. This eliminates the need for manual translation of problem statements into solver code, enabling rapid prototyping and interactive exploration.

The server solves a wide spectrum of constraint satisfaction and optimization tasks. It supports integer and boolean variables, linear arithmetic constraints using familiar , , , and syntax, and linear objectives for maximization or minimization. Timeouts and solver parameters can be tuned per request, allowing developers to balance speed against solution quality. Because the interface is purely JSON‑based, any language that can produce or consume JSON—Python, JavaScript, Rust, etc.—can act as a client, making MCP‑ORTools a versatile component in heterogeneous AI pipelines.

Key capabilities include:

• Full CP‑SAT support: leverage the most powerful open‑source solver for Boolean, integer, and linear problems.
• Model validation: the server checks syntax and variable consistency before invoking OR‑Tools, providing early error feedback.
• Rich solution reporting: status codes (, , , ) and execution time accompany the variable assignments, facilitating debugging and performance monitoring.
• Extensible constraint syntax: basic arithmetic operations (, , ) combined with OR‑Tools method calls allow expressive modeling of real‑world constraints such as resource limits, precedence relations, or mutual exclusions.

Typical use cases span operations research and AI‑augmented decision making. In supply chain management, an assistant can generate a knapsack or vehicle routing model on the fly and retrieve the optimal allocation. In scheduling, it can encode workforce constraints (e.g., no overtime beyond a threshold) and produce feasible shift plans. Educational tools can let students experiment with constraint programming directly from chat, receiving instant feedback on model correctness and solution quality.

Integrating MCP‑ORTools into an AI workflow is straightforward: a language model generates the JSON specification, sends it via MCP to the server, and then interprets the returned solution. Because the protocol standardizes request and response formats, developers can chain multiple MCP services—such as a natural‑language parser, a data retrieval tool, and the OR‑Tools solver—to build end‑to‑end decision support systems. The result is a powerful, low‑friction workflow that empowers AI assistants to solve real optimization problems with the same ease as answering trivia questions.