diff --git a/library/cadbiom/models/clause_constraints/mcl/MCLAnalyser.py b/library/cadbiom/models/clause_constraints/mcl/MCLAnalyser.py
index d168c5924f730e946d0247db9aa4c1fa2a1e90ad..9218924fc3fdf3576b275686b8a5d82b6a1b39a6 100644
--- a/library/cadbiom/models/clause_constraints/mcl/MCLAnalyser.py
+++ b/library/cadbiom/models/clause_constraints/mcl/MCLAnalyser.py
@@ -378,7 +378,7 @@ class MCLAnalyser(object):
         reduce the number of activated frontier variables from this solution while
         inducing "prop".
 
-        Find at most nb_sols_to_be_pruned frontier solutions inducing
+        Find at most `nb_sols_to_be_pruned` frontier solutions inducing
         the same final property but with all inactivated frontier places
         forced to be initially False.
 
@@ -470,11 +470,10 @@ class MCLAnalyser(object):
     def sq_solutions(self, query, max_step, max_sol, vvars):
         """Return a list of RawSolution objects
 
-        Parameters are the same as in sq_is_satisfiable() except for vvars
-        parameter which deserves some explanations.
-
         This function is the lowest level function exploiting the solver results.
 
+        Parameters are the same as in sq_is_satisfiable() except for vvars
+        parameter which deserves some explanations.
 
         The solver doesn’t provide all solutions of the set of logical constraints
         encoding the temporal property. It gives only a sample of these solutions
@@ -677,6 +676,13 @@ class MCLAnalyser(object):
     def next_mac(self, query, max_step):
         """Search a Minimal Access Condition for the given query.
 
+        This is a function very similar to __mac_exhaustive_search(), but it
+        returns only 1 solution.
+        Satisfiability tests and the banishment of previous solutions must be
+        done before the call.
+
+        ---
+
         On cherche ensuite d'abord des solutions non minimales (actuellement 2) via:
         self.__sq_dimacs_frontier_solutions(query, nb_step, 2)