Swordfish studies the information yield of counting experiments. It implements at its core a rather general version of a Poisson point process with background uncertainties described by a Gaussian random field, and provides easy access to its information geometrical properties. Based on this information, a number of common and less common tasks can be performed. Swordfish allows quick and accurate forecasts of experimental sensitivities without time-intensive Monte Carlos, mock data generation and likelihood maximization. It can:

- calculate the expected upper limit or discovery reach of an instrument;
- derive expected confidence contours for parameter reconstruction;
- visualize confidence contours as well as the underlying information metric field;
- calculate the information flux, an effective signal-to-noise ratio that accounts for background systematics and component degeneracies; and
- calculate the Euclideanized signal which approximately maps the signal to a new vector which can be used to calculate the Euclidean distance between points.