The rapid proliferation of Generative AI has created a pressing need for rigorous documentation standards that support transparency and governance. Manual creation of Model and Data Cards, however, is difficult to scale, and automated approaches lack the large-scale, high-fidelity benchmarks required for systematic evaluation. We introduce MetaGAI, a benchmark of 2,541 semantically matched document triplets constructed from academic papers, GitHub repositories, and Hugging Face artifacts. Reference cards are produced by a multi-agent pipeline of specialized Retriever, Generator, and Editor agents, and the pipeline is assessed through a four-part validation protocol. We further propose an evaluation framework that combines reference-based automated metrics with a multi-model LLM-as-a-Judge protocol, together with explicit agreement and sensitivity analyses. Among the evaluated baselines, Qwen3-30B-A3B-Instruct achieves the best observed cost–quality trade-off, and the results reveal a recurring tension between faithfulness and completeness. MetaGAI provides a testbed for benchmarking, training, and analyzing automated Model and Data Card generation at scale. Our data and code are available at https://github.com/haoxuan-unt2024/MetaGAI-Benchmark.