Trust is an essential aspect of human-robot interaction. For smooth and safe interactions, a calibrated level of trust – a state in which a human’s level of trust matches their robotic counterpart's capabilities – is needed. While an obvious strategy to correct possible miscalibration is transparency about the system's capabilities, previous studies are inconclusive: In some cases, the use of transparency led to an increased level of trust, in other situations, the level of trust dropped significantly. Our hypothesis is that the inconclusive results are due to the fact that what capabilities a robot is transparent about influences the effect of transparency on trust calibration. Our research therefore investigates whether transparency utterances that differ in content can either raise or dampen the perceived level of trust and competence of a robot. In an online experiment, we tested three different verbal strategies for trust calibration. Conditions differ regarding different aspects of capability the robot is transparent about: One condition uses transparency about a low level of capability, another condition uses transparency about a high level of capability. The third condition tested a robot with no additional transparency. In addition, we tested a baseline condition that used acoustic feedback only. Participants evaluated videos of four different robots, each in one of the four different conditions. We used the Multi-Dimensional Measure of Trust scale [1] to capture the perceived level of trust. Altogether 100 subjects, recruited via Prolific, participated in the experiment. Our results confirm our assumption that it matters what capabilities the robot is transparent about. Further, we confirmed that the perceived level of robots’ competence is affected by the transparency strategies used.