Sure! To generate test cases for the Convex Hull problem, we'll need to produce random sets of points within the given constraints. Here's a Python function to generate such test cases:

```python
import random

def tcgen():
    # Number of points
    n = random.randint(3, 100000)
    
    # Generate a set of unique points within the range [-10000, 10000]
    points = set()
    while len(points) < n:
        x = random.randint(-10000, 10000)
        y = random.randint(-10000, 10000)
        points.add((x, y))
    
    # Convert the set of points to a list to maintain order
    points_list = list(points)
    
    return n, points_list

# Example usage:
n, points = tcgen()
print(n)
for x, y in points:
    print(x, y)
```

### Explanation:

1. **Number of Points**: Randomly select the number of points \( n \) between 3 and 100,000.
2. **Generate Points**: Use a set to ensure all points are unique. Randomly generate \( x \) and \( y \) coordinates within the range [-10,000, 10,000]. Continue adding points to the set until we have \( n \) unique points.
3. **Return Points**: Convert the set to a list for ordered output.

This approach ensures we respect the constraints and provide valid test cases for the Convex Hull problem. 

### Output Format:
- The first line is the number of points \( n \).
- The following \( n \) lines contain the coordinates of each point.

Feel free to run the function and generate test cases for your problem!