diff --git a/README.md b/README.md
index 930fe50f..1368a61b 100644
--- a/README.md
+++ b/README.md
@@ -111,8 +111,10 @@ Attribute   | Type               | Notes
 `jfree`     | `Vector{Int}`      | indices of "free" constraints (there shouldn't be any)
 `jinf`      | `Vector{Int}`      | indices of the visibly infeasible constraints
 `nnzo`      | `Int             ` | number of nonzeros in the gradient
-`nnzj`      | `Int             ` | number of nonzeros in the sparse Jacobian
 `nnzh`      | `Int             ` | number of nonzeros in the sparse Hessian
+`nnzj`      | `Int             ` | number of nonzeros in the sparse Jacobian
+`lin_nnzj`  | `Int             ` | number of nonzeros in the linear part of sparse Jacobian
+`nln_nnzj`  | `Int             ` | number of nonzeros in the nonlinear part of sparse Jacobian
 `minimize`  | `Bool            ` | true if `optimize == minimize`
 `islp`      | `Bool            ` | true if the problem is a linear program
 `name`      | `String`           | problem name
diff --git a/src/nlp/api.jl b/src/nlp/api.jl
index 4ba6bc60..46812e8e 100644
--- a/src/nlp/api.jl
+++ b/src/nlp/api.jl
@@ -1344,6 +1344,30 @@ function hess_op!(
   return LinearOperator{T}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod!, prod!, prod!)
 end
 
+"""
+    varscale(model::AbstractNLPModel)
+
+Return a vector containing the scaling factors for each variable in the model.
+This is typically used to normalize variables for numerical stability in solvers.
+
+By default, the scaling is model-dependent. If not overridden by the model, a vector of ones 
+is returned. Inspired by the AMPL scaling conventions.
+"""
 function varscale end
+
+"""
+    lagscale(model::AbstractNLPModel)
+
+Return a vector of scaling factors for the Lagrange multipliers associated with constraints.
+This can be used to improve numerical stability or condition number when solving KKT systems.
+"""
 function lagscale end
+
+"""
+    conscale(model::AbstractNLPModel)
+
+Return a vector of constraint scaling factors for the model.
+These are typically used to normalize constraints to have similar magnitudes and improve 
+convergence behavior in nonlinear solvers.
+"""
 function conscale end
diff --git a/src/nlp/show.jl b/src/nlp/show.jl
index 7970e4be..ecc6f8c7 100644
--- a/src/nlp/show.jl
+++ b/src/nlp/show.jl
@@ -68,7 +68,7 @@ end
 
 Describe `meta` for the `show` function.
 """
-function lines_of_description(m::AbstractNLPModelMeta)
+function lines_of_description(m::M) where {M <: AbstractNLPModelMeta}
   V = [
     length(m.ifree),
     length(m.ilow),
@@ -93,8 +93,22 @@ function lines_of_description(m::AbstractNLPModelMeta)
   V = [sum(V); V]
   S = ["All constraints", "free", "lower", "upper", "low/upp", "fixed", "infeas"]
   conlines = lines_of_hist(S, V)
-  push!(conlines, histline("linear", m.nlin, m.ncon), histline("nonlinear", m.nnln, m.ncon))
-  push!(conlines, sparsityline("nnzj", m.nnzj, m.nvar * m.ncon))
+
+  append!(
+    conlines,
+    [
+      histline("linear", m.nlin, m.ncon),
+      histline("nonlinear", m.nnln, m.ncon),
+      sparsityline("nnzj", m.nnzj, m.nvar * m.ncon),
+    ],
+  )
+
+  if :lin_nnzj in fieldnames(M)
+    push!(conlines, sparsityline("lin_nnzj", m.lin_nnzj, m.nlin * m.nvar))
+  end
+  if :nln_nnzj in fieldnames(M)
+    push!(conlines, sparsityline("nln_nnzj", m.nln_nnzj, m.nnln * m.nvar))
+  end
 
   append!(varlines, repeat([" "^length(varlines[1])], length(conlines) - length(varlines)))
   lines = varlines .* conlines
diff --git a/test/nlp/show.jl b/test/nlp/show.jl
index 9ce7e6f3..281b9614 100644
--- a/test/nlp/show.jl
+++ b/test/nlp/show.jl
@@ -16,6 +16,8 @@
             nnzh: ( 33.33% sparsity)   2               linear: ██████████⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 1
                                                     nonlinear: ██████████⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 1
                                                          nnzj: (  0.00% sparsity)   4
+                                                     lin_nnzj: (  0.00% sparsity)   2
+                                                     nln_nnzj: (  0.00% sparsity)   2
 
   Counters:
              obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 grad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 cons: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
@@ -39,7 +41,9 @@
           fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
          infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
            nnzh: (  0.00% sparsity)   1               linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
-                                                   nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
-                                                        nnzj: (------% sparsity)\n\n"""
+                                                   nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0           
+                                                        nnzj: (------% sparsity)
+                                                    lin_nnzj: (------% sparsity)
+                                                    nln_nnzj: (------% sparsity)\n\n"""
   @test strip.(split(chomp(showed), "\n")) == strip.(split(chomp(expected), "\n"))
 end
diff --git a/test/nls/show.jl b/test/nls/show.jl
index 62766237..6f25aaab 100644
--- a/test/nls/show.jl
+++ b/test/nls/show.jl
@@ -16,6 +16,8 @@
             nnzh: (  0.00% sparsity)   3               linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0
                                                     nonlinear: ████████████████████ 3
                                                          nnzj: (  0.00% sparsity)   6
+                                                     lin_nnzj: (------% sparsity)         
+                                                     nln_nnzj: (  0.00% sparsity)   6
 
   Counters:
              obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 grad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 cons: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0