I would like to attack this a bit more systematically:

Querying the core database alone (noting that the core observations/datapoints are “flows”):

 (BW_Q1) What is the direct input flow of flow-object F to activity A measured by flow-property P in location L in the time period T under macro-economic scenario S?

(example: What is the direct input of coal to steel production measured by dry mass in Germany in year 2020 under the Business-as-Usual scenario?)

(BW_Q2) What is the direct output flow of flow-object F from activity A measured by flow-property P in location L in the time period T under macro-economic scenario S?

(example: What is the direct output of steel from steel production measured by EUR2011 nominal value in Germany in year 2020 under the Business-as-Usual scenario?)

(BW_Q3) What is the determining flow of activity A in location L in the time period T under macro-economic scenario S?

(example: What is the determining flow of soybean mills in Brazil in year 2020 under the Business-as-Usual scenario?)

(BW_Q4) What is the difference between input flows and output flows of flow-property P for activity A in location L in the time period T under macro-economic scenario S?

            (example: What is the difference between input flows and output flows of dry mass for all activities globally in year 2020 under the Business-as-Usual scenario?)

Querying the core database together with a system model algorithm (for creating product footprints):

(BW_Q5) What is the additional input flow of flow-object F to activity A measured by flow-property P in location M in the time period T resulting from a demand of flow-object G in location M in the time period U, all under macro-economic scenario S?

(example: What is the additional input of surface water to steel production measured by wet mass in Germany in year 2020 resulting from a demand of 100 square meter of office building in Spain in year 2019 under the Business-as-Usual scenario?)

(BW_Q6) What is the additional output flow of flow-object F from activity A measured by flow-property P in location M in the time period T resulting from a demand of flow-object G in location M in the time period U, all under macro-economic scenario S?

(example: What is the additional radiative forcing from the atmospheric energy transfer measured by power per area globally in year 2020 resulting from a demand of 100 square meter of office building in Spain in year 2019 under the Business-as-Usual scenario?)

I will break the rule of not commenting, by pointing out that:

(ML_Q1) is an instance of (BW_Q3)
(ML_Q2) is an instance of (BW_Q5)
(ML_Q3) is an instance of (BW_Q2)
(ML_Q4) and (ML_Q5) are queries for the location of specific agents or activities, and thus possible and relevant queries, but not related to the flows that is the core observations in the database

(AG_Q1) can be obtained as an instance of either of (BW_Q1), (BW_Q2), (BW_Q5) and (BW_Q6) (AG_Q2) is similar to (BW_Q4)
(AG_Q3) is an instance of (BW_Q6)