Ensure you have Python installed on your machine, as well as the pandas and pyodbc libraries. Python is versatile for handling Parquet files, and pyodbc is used for connecting to MS SQL Server.

Use the pandas library to read the Parquet file. You can do this by importing pandas and using the `read_parquet` function. This will load the data into a DataFrame, which is an in-memory representation of the data.
```python
import pandas as pd
# Load the Parquet file into a DataFrame
df = pd.read_parquet('your_file.parquet')
```

Ensure your MS SQL Server is running and you have the necessary credentials and permissions to create tables and insert data. Create a table that matches the schema of your DataFrame. This can be done using SQL Server Management Studio (SSMS) or any other SQL client.

Use the pyodbc library to establish a connection to your MS SQL Server. You will need the server name, database name, user credentials, and any other connection parameters.
```python
import pyodbc
# Establish a connection to the MS SQL Server
conn = pyodbc.connect(
'DRIVER={ODBC Driver 17 for SQL Server};'
'SERVER=your_server_name;'
'DATABASE=your_database_name;'
'UID=your_username;'
'PWD=your_password'
)
```

Check if any data transformation is needed to ensure compatibility with the SQL Server schema. This may include data type conversions or handling missing values. Use pandas functions to perform necessary transformations.
```python
# Example: Convert a column to a specific data type
df['column_name'] = df['column_name'].astype('desired_data_type')
```

Use a cursor object from the pyodbc connection to execute SQL queries. You can iterate over the DataFrame and insert rows one by one or use the `to_sql` method from pandas with SQLAlchemy for bulk insert if desired, but ensure it’s within the scope of using built-in tools.
```python
cursor = conn.cursor()
# Insert data row by row
for index, row in df.iterrows():
cursor.execute('''
INSERT INTO your_table_name (column1, column2, ...)
VALUES (?, ?, ...)
''', row['column1'], row['column2'], ...)
# Commit the transaction
conn.commit()
```

After the data insertion, verify that the data has been transferred correctly by querying the SQL Server table. Finally, close the connection and clean up any resources used during the process.
```python
# Verify the data
cursor.execute('SELECT COUNT(*) FROM your_table_name')
print(cursor.fetchone())
# Close the connection
conn.close()
```
By following these steps, you can effectively move data from a Parquet file to MS SQL Server using Python’s built-in libraries without relying on third-party connectors.