I agree with @ERT (+1) that just giving the sample mean and standard deviation
might give a false or confusing impression of your data, and that some
graphical presentation that shows the two outliers (such as a histogram, with or without the smoothing density estimator) the would be useful. My purpose here is to discuss additional possibilities.
Without knowing the source of your data or the audience for your summary of them, it is difficult to give advice about what summary would be meaningful.
Here is a listing of your $n = 29$ observations, sorted from smallest to largest:
 5 9 10 15 19 20 31 34 35 56
 59 67 67 77 99 105 122 140 140 144
 150 160 177 188 199 250 340 1490 1680
If limited to a brief numerical summary, I would say the 29 observations
run from a minimum of 5 to a maximum of 1680, with a sample median of 99.
Also, that all but two observations are 340 or smaller. If your listing
puts data in time order of collection, it may be worthwhile
to mention that the two extreme observations 1490 and 1680 occurred near the start (the second
and third observations).
If the audience for your summary is familiar with statistical terminology
it may be useful to give the summary statistics provided by the function
summary in R:
Min. 1st Qu. Median Mean 3rd Qu. Max.
5 34 99 203 160 1680
Then a boxplot might also be useful (but only if you wouldn't need to take half a page
to explain what a boxplot is).
boxplot(x, horizontal=T, col="skyblue2", pch=19)
In any case, if you have a any clue as to the possible reason for the two
extreme outliers, you might include a possible explanation.
For statistically more sophisticated audiences, it might be useful to show
the nonparametric 95% confidence interval $(72, 152)$ for the population median that can be found in R using
the Wilcoxon signed-rank method
wilcox.test(x, conf.int=T, conf.lev=.95).
Or to provide a 95% nonparametric bootstrap confidence interval for the population mean (which I did not compute).