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Model and solve constraint and optimization problems via JSON using Google OR-Tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP-ORTools" yet — see the docs or source repo.
Use MCP-ORTools to build a staff scheduling optimization model: 12 employees, 7 days, two shifts per day; at least 3 people per shift; each person can work at most 5 shifts per week and cannot take late shifts on consecutive days; optimize for balanced workload while satisfying all constraints. Output the JSON model and a summary of the solution.
A runnable JSON constraint model plus a feasible schedule and objective-value summary.
Use MCP-ORTools to solve a vehicle routing problem: 5 vehicles depart from one depot to serve 30 stops, each with demand, and each vehicle has a capacity limit. Minimize total travel distance. Output the JSON model, key constraints, and the optimal or near-optimal routes.
A vehicle-routing optimization model, key parameter details, and route results for each vehicle.
Use MCP-ORTools to build a project resource allocation model: 8 tasks, 4 engineers, each task has effort and a deadline, each engineer has different skills and limited daily capacity. Minimize delay while satisfying skill and time constraints. Output the JSON model and allocation recommendations.
A resource allocation optimization model plus task-to-person assignments and delay analysis.
Orchestrate multiple MCP server tools for complex Python workflows with logic.
Use natural language to run 58 online tools and workflows.
Build, debug, and manage software tasks with natural language across LLMs.
Turn multiple LLMs into MCP tools with JSON, no Python required.
Generate MCP tools from OpenAPI specs for natural-language API interaction.
Connect Google Workspace apps to automate email, documents, calendars, and tasks.