MCP‑ORTools – Constraint Solving for AI Assistants

MCP‑ORTools bridges the gap between large language models and sophisticated constraint‑programming engines by exposing Google OR‑Tools’ CP‑SAT solver through the Model Context Protocol. Developers can now ask an AI assistant to formulate, validate, and solve complex scheduling, routing, or optimization problems without leaving the conversational interface. The server accepts a JSON‑encoded model that lists variables, linear constraints written in OR‑Tools syntax, and an optional objective. This tight integration eliminates the need for manual translation between natural language specifications and solver‑specific APIs, enabling rapid prototyping and iterative refinement directly within the assistant’s workflow.

The server’s core value lies in its standardized model specification. Variables are defined with clear domains, constraints use the intuitive , , , and operators, and objectives are expressed as simple arithmetic expressions. By adhering to a common schema, any MCP‑compatible client—Claude Desktop, LangChain, or custom tooling—can submit a model and receive a structured JSON response containing status, solve time, variable assignments, and objective value. This predictability makes it straightforward to parse results, feed them back into downstream processes, or visualize solutions within dashboards.

Key capabilities include:

• Full CP‑SAT support: Handles integer, boolean, and linear constraints with built‑in optimization.
• Timeouts and solver parameters: Allows fine‑tuning of search limits for time‑critical applications.
• Binary and portfolio selection logic: Enables modeling of knapsack, scheduling, or resource allocation problems.
• Linear objective handling: Supports both maximization and minimization objectives expressed in plain arithmetic.

Typical use cases span from simple educational examples—such as selecting items for a knapsack—to enterprise‑grade resource planning, employee rostering, and supply‑chain optimization. An AI assistant can guide a user through defining constraints, automatically validate syntax errors, and present the optimal solution in natural language or as a structured data table. Because the server communicates over HTTP and follows MCP conventions, it can be embedded in existing AI pipelines or invoked on demand from chat interfaces.

MCP‑ORTools stands out by offering a zero‑code pathway to powerful constraint solving. Developers who already rely on language models for problem formulation can now leverage a proven solver without writing custom adapters. The combination of JSON‑based models, clear operator semantics, and comprehensive CP‑SAT features makes it an ideal tool for any scenario where precise constraint satisfaction or optimization is required within an AI‑driven workflow.