diff --git a/src/SyntheticDatasets.jl b/src/SyntheticDatasets.jl
index e1fcbbf..9ebfe23 100644
--- a/src/SyntheticDatasets.jl
+++ b/src/SyntheticDatasets.jl
@@ -12,6 +12,7 @@ end
 
 include("sklearn.jl")
 include("matlab.jl")
+include("descriptor.jl")
 
 function convert(features::Array{T, 2}, labels::Array{D, 1})::DataFrame where {T <: Number, D <: Number}
     df = DataFrame()
diff --git a/src/descriptor.jl b/src/descriptor.jl
new file mode 100644
index 0000000..25eeeef
--- /dev/null
+++ b/src/descriptor.jl
@@ -0,0 +1,102 @@
+mutable struct MethodDescriber
+    name::String
+    description::Union{String, Nothing}
+    problem_type::Union{Symbol, Nothing}
+    f::Union{Function, Nothing}
+        
+    MethodDescriber() = new()
+    
+end
+
+function MethodDescriber(   name::String;
+                            description = nothing,
+                            problem_type = nothing,
+                            f = nothing)
+    
+    method = MethodDescriber()
+
+    method.name = name
+    method.description = description
+    method.problem_type = problem_type
+    method.f = f
+    return method
+end 
+
+function Base.show(io::IO, method::MethodDescriber)
+    println(io, "$(method.name)")
+    method.problem_type !== nothing && println(io, "problem type: " * string(method.problem_type))
+    method.description !== nothing && println(io, "Description:\n" * method.description) 
+end
+
+mutable struct MethodDescriberSet
+    describers::Array{MethodDescriber, 1}
+end
+
+MethodDescriberSet(args...) = MethodDescriberSet([args...])
+
+function methodsFilter(methods::MethodDescriberSet, parameters::Union{Pair, Array{Pair}})
+    if !(parameters isa Array)
+        parameters = [parameters]
+    end
+
+    filtered_methods = Set()
+    
+    for parameter in parameters
+        if !(parameter[1] in fieldnames(MethodDescriber))
+            @warn "$(parameter[1]) isn't a property of MethodDescriber"
+            continue
+        end
+        for method in methods.describers 
+            property = getfield(method, parameter[1])
+            if property == parameter[2]
+                push!(filtered_methods, method)
+            end
+        end
+    end
+
+    return MethodDescriberSet(collect(filtered_methods))
+end
+
+methodsFilter(parameters::Union{Pair, Array{Pair}}) = methodsFilter(METHODS, parameters)
+
+Base.getindex(methods::MethodDescriberSet, index::Int) = methods.describers[index]
+
+function Base.show(io::IO, methods::MethodDescriberSet)
+    for method in methods.describers
+        print(io, method)
+    end
+end
+
+const METHODS = MethodDescriberSet(
+    MethodDescriber(
+        "generate_blobs",
+        problem_type = :Classification,
+        description = """
+        Generate isotropic Gaussian blobs for clustering. Sklearn interface to make_blobs.
+        """,
+        f = generate_blobs,),
+    MethodDescriber(
+        "generate_moons",
+        problem_type = :Classification,
+        description = """
+        Generate isotropic Gaussian blobs for clustering. Sklearn interface to make_blobs.
+        """,
+        f = generate_moons,),
+    MethodDescriber(
+        "make_s_curve",
+        problem_type = :Regression,
+        description = """
+        Generate an S curve dataset. Sklearn interface to make_s_curve.
+        """,
+        f = generate_s_curve,),
+    MethodDescriber(
+        "generate_regression",
+        problem_type = :Regression,
+        description = """
+        Generate a random regression problem. Sklearn interface to make_regression.
+        """,
+        f = generate_regression,)
+  
+)
+
+methods() = METHODS
\ No newline at end of file
diff --git a/test/describer.jl b/test/describer.jl
new file mode 100644
index 0000000..7850d32
--- /dev/null
+++ b/test/describer.jl
@@ -0,0 +1,81 @@
+using SyntheticDatasets
+using DataFrames
+using Test
+
+@testset "SkLearn Generators" begin
+    samples = 20000
+    features = 20
+
+    data = SyntheticDatasets.generate_blobs(centers = [-1 1;-0.5 0.75], 
+                                            cluster_std = 0.225, 
+                                            n_samples = 20000,
+                                            center_box = (-1.5, 1.5))
+    
+    @test size(data)[1] == samples
+    @test size(data)[2] == 3
+
+    samples = 20000
+    data = SyntheticDatasets.generate_moons(n_samples = 20000)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 3
+
+    data = SyntheticDatasets.generate_s_curve(n_samples = samples,
+                                              noise = 2.2,
+                                              random_state = 5)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 4
+
+    data = SyntheticDatasets.generate_circles(n_samples = samples)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 3
+
+    data = SyntheticDatasets.generate_regression(n_samples = samples,
+                                                 n_features = features,
+                                                 noise = 2.2,
+                                                 random_state = 5)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == features + 1
+
+    data = SyntheticDatasets.generate_classification(n_samples = samples,
+                                                    n_features = features,
+                                                    n_classes = 1)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == features + 1
+
+    data = SyntheticDatasets.generate_friedman1(n_samples = samples,
+                                                    n_features = features)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == features + 1
+
+    data = SyntheticDatasets.generate_friedman2(n_samples = samples)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 5
+
+    data = SyntheticDatasets.generate_friedman3(n_samples = samples)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 5
+
+    data = SyntheticDatasets.generate_low_rank_matrix(n_samples = samples,
+                                                    n_features = features,
+                                                    effective_rank = 10,
+                                                    tail_strength = 0.5,
+                                                    random_state = 5)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == features
+    
+    data = SyntheticDatasets.generate_swiss_roll(n_samples =samples,
+                                                 noise = 2.2,
+                                                 random_state = 5)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 4
+end