In agricultural tasks, manual grape harvesting remains a labor-intensive activity facing challenges of efficiency, labor shortages, and sustainability. To this end, tailor-made robotic systems have been designed with the capabilities to transfer heavy boxes, navigate vineyard terrains, communicate, accurately locate, and safely interact with humans. The introduction of collaborative robotic fleets alongside human workers in large-scale vineyard harvesting effectively presents a Multi-Robot Task Allocation (MRTA) problem, where the real-world domain possesses characteristics that, when combined, pose a challenging research endeavor and align with open issues in MRTA research. Here, we present RoboVineSim, a simulation tool that can capture any vineyard area using geographical data and model the behavior of humans and robots in the environment. In addition, we have established the necessary mechanisms to facilitate the development of novel MRTA methods in this domain.