Synthetic data generation is a key strategy for addressing few-shot time-series tasks. However, existing methods struggle to synthesize diverse, realistic data and lack the fine-grained output control required for effective application in real-world time-series downstream tasks. In this paper, we propose a multi-agent framework that addresses these challenges by leveraging multimodal analysis to capture real-world data patterns from limited samples, while simultaneously enabling fine-grained control over the final output through natural language. To ensure Large Language Models (LLMs) can effectively comprehend the intricate characteristics of time-series data, we design a multimodal analysis agent to interpret both the numerical features and visual patterns of the raw data, translating these into rich textual descriptions. This textual analysis, fused with objectives and domain knowledge refined by an expert agent, creates precise instructions that direct a code-generating agent to programmatically synthesize the data. A discriminator agent then assesses the synthesized data’s alignment with these instructions, providing feedback that drives the framework’s iterative refinement loop. Our framework significantly outperforms baselines, reducing forecasting task MSE by up to 22.1%, improving classification task accuracy by 16.7%, and boosting anomaly detection task F1-scores by over 17%.