This work introduces a prompt-based framework to address the data bottleneck in battery research where high data volumes typically require rare expertise in electrochemistry and programming. By using an LLM-driven agent orchestrated via Agno, the system translates natural language into executable pipelines for querying live databases. A dynamic knowledge base provides essential laboratory context, allowing the agent to navigate complex data dependencies.