Audits are critical mechanisms for identifying the risks and limitations of deployed artifcial intelligence (AI) systems.However, the efective execution of AI audits remains incredibly difcult, and practitioners often need to make use of various tools to support their eforts.Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ecosystem of AI audit tooling to practitioner needs.While many tools are designed to help set standards and evaluate AI systems, they often fall short in supporting accountability.We outline challenges practitioners faced in their eforts to use AI audit tools and highlight areas for future tool development beyond evaluationfrom harms discovery to advocacy.We conclude that the available resources do not currently support the full scope of AI audit practitioners' needs and recommend that the feld move beyond tools for just evaluation and towards more comprehensive infrastructure for AI accountability.