Ensuring fair access to public administrative procedures is a significant challenge in e-government, due to their inherent complexity and the lack of standardized formats for textual descriptions. While open data initiatives have improved accessibility and transparency, many citizens and public sector employees still struggle to effectively interpret and utilize procedural information. To address this issue, we propose AdmPModeler, a novel tool that leverages Large Language Models, LLMs, to automatically generate process models from legal texts describing administrative procedures. Our tool employs advanced prompt engineering techniques —including prompt chaining, chain-of-thought reasoning, role prompting, and LLMs-as-a-judge— to enhance the quality and reliability of process modeling. We validate AdmPModelerthrough a human evaluation conducted in collaboration with domain experts from the Italian Department for Public Administration. The assessment compares the structured procedures extracted by the tool with their original textual descriptions, demonstrating its effectiveness in formally and clearly representing administrative procedures in a human-readable structured format.