To generate test cases for the Convex Hull problem, you need to ensure that the points are distinct and within the specified range. Here’s a Python function that generates test cases compliant with the given problem statement:

```python
import random

def tcgen():
    # Number of points, constrained between 3 and 100000
    n = random.randint(3, 100000)
    
    # Ensure uniqueness of points by using a set initially
    points_set = set()
    
    while len(points_set) < n:
        x = random.randint(-10000, 10000)
        y = random.randint(-10000, 10000)
        points_set.add((x, y))
    
    # Convert the set to a list for easier manipulation
    points_list = list(points_set)
    
    return n, points_list

# Example of generating a test case and printing it
n, points = tcgen()
print(n)
for point in points:
    print(point[0], point[1])
```

### Explanation:
1. **Number of Points (`n`)**: Randomly generate an integer between 3 and 100,000.
2. **Generating Points**: Use a set to ensure all points are unique. Randomly generate `x` and `y` coordinates within the range of -10,000 to 10,000.
3. **Ensure Uniqueness**: Add points to a set until the desired number of unique points (`n`) is achieved.
4. **Convert to List**: Convert the set of points to a list for easier handling and output.

This code ensures that all constraints are met, including uniqueness of points and specified ranges. The generated test cases can be used to test the convex hull algorithm comprehensively.