Depending on your data, you may be overfitting, however that isn't necessarily the definitive answer.
Gradient boosted trees are a powerful algorithm and for a while performed as state-of-the-art. If your data happen to represent the target value in a systematic way that you haven't uncovered yet, it's likely that with 500 estimating trees the algorithm found a perfect solution. It's not unheard of.
On the other hand, I don't know much about your data. How many samples do you have? 100? 100,000? The former will be much easier to perfectly model. The latter may also be predictable (albeit less likely) if the variance between classes is predictable. The number of features may also play a role, and the significance of each feature.
As suggested in the comments, Cross Validation may help you discover what's going on here. I highly suggest reading the paper I linked above to see an example of rigorous CV. Carefully read what they did to see how you can model your own CV setup.
You might consider checking out the feature importance returned by your classifier. If one feature is significantly important, it might indicate a close correlation between that feature and the target variable (which should indicate that you need to take a close look at that feature).