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MCP integration for Timefold Solver
An open MCP integration that lets AI assistants use Timefold Solver for schedules, assignments, and other optimisation tasks.
An open MCP integration that lets AI assistants use Timefold Solver for schedules, assignments, and other optimisation tasks.
What the project does
solver-mcp is not a solver of its own. It connects AI assistants to Timefold Solver through MCP. The AI collects the request and explains the result, while Timefold handles the actual planning and optimisation.
Why this connection is useful
An AI assistant can understand requirements expressed in natural language. For tasks such as staff schedules, appointment allocation, or sharing limited resources, however, a plausible answer is not enough: every rule has to be considered at the same time.
This is where Timefold complements the assistant. It calculates a dependable assignment instead of merely suggesting one in words.
How it works
- Understand the request — the AI assistant gathers the goal, rules, and available resources
- Pass it to Timefold — the MCP integration connects the assistant to the solver
- Calculate a solution — Timefold finds an assignment that takes the given rules into account
- Explain the result — the assistant returns the solution in a clear, useful form
Possible uses
The integration is useful wherever many requirements meet: staff and appointment scheduling, task or capacity allocation, and seating or room assignments.
Open source for everyone
The project is freely available under the Apache 2.0 license. The repository includes the integration, documentation, and examples and can be used, adapted, and extended for other projects.
Technologies
Kotlin, MCP, Timefold Solver, Docker, Gradle