Without more information on the context of this code and the purpose it serves, it is difficult to optimize it effectively. However, there are some general tips that can improve its performance:

1. Use list comprehension instead of loops: Instead of using a for loop to append points to the mdr_points list, you can use a list comprehension to perform the same operation more efficiently. For example:
```
def is_mdr(line):
    response = requests.post(
        MDR_URL,
        data=line.wkt,
        headers={"accept": "/", "Content-Type": "text/plain"}
    )
    return response.content == b"true"

mdr_points = [points for points in split_list(route_points) if is_mdr(shapely.geometry.LineString(points))]
```

2. Use the built-in 'in' operator to check for membership: Instead of using len() to check if the mdr_points list is non-empty, you can use the 'in' operator to check for membership. For example:
```
if mdr_points:
    mdr_distance += doe.get_4326_geometry_length_in_m(shapely.LineString(mdr_points))
    mdr_points = []
```

3. Use a generator expression to avoid creating a temporary list: Instead of creating a temporary list of points to pass to the shapely.LineString() constructor, you can use a generator expression to avoid creating unnecessary objects. For example:
```
mdr_distance += doe.get_4326_geometry_length_in_m(shapely.LineString(point for point in mdr_points))
```

4. Cache the LineString object for reuse: Instead of creating a new LineString object on each iteration of the loop, you can cache the object and reuse it. For example:
```
line_cache = {}
...
for points in split_list(route_points):
    ...
    line_wkt = line.wkt
    if line_wkt not in line_cache:
        line_cache[line_wkt] = line
    is_mdr = (
        requests.post(
            MDR_URL,
            data=line_wkt,
            headers={"accept": "/", "Content-Type": "text/plain"},
        ).content
        == b"true"
    )
    ...
    mdr_distance += doe.get_4326_geometry_length_in_m(line_cache[line_wkt])
```
These are just a few examples of possible optimizations that may or may not be applicable to your specific use case. It is important to benchmark and test your code after making any changes to ensure that they actually improve performance.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence