It can interpret informal ranking language like 'the ones who order the most and spend the most' and launch the analysis directly.
What was measured
Plain English Query Handling
Can the tool understand business questions without SQL?
decisive for this rankingtransformation
This is the core of the ranking: the tool must understand a business question without the user writing SQL. (3 of 3 judges)
What was given, what came back
Test input: Best customers with unpaid-order and payment-method follow-ups · text · group: live-database-plain-english-queries
Input — what we sent
The exact prompt
Who are my best customers — the ones who order the most and spend the most? Follow-up 1: For the top 3 from that list — do any of them have unpaid orders? Follow-up 2: What payment methods do these top 3 usually use?
A conversational multi-table customer analysis with follow-up questions. It asks for the best customers by both order volume and spend, then drills into unpaid orders for the top 3 and their usual payment methods. Designed to test ranking logic, join-heavy analysis, and follow-up context retention.
Why this input is hard
- · ambiguous business term interpretation
- · multi-table joins
- · aggregation and ranking
- · follow-up context retention
- · scoping to a selected subset
- · payment behavior analysis
Output — unretouched
Also checked on this input — same tool, 5 other criteria
Ambiguity Handling✓ WorkedIt handles the ambiguous 'best customers' request by surfacing both order-count and spend rankings instead of silently choosing one metric.Business Insight✓ WorkedIt adds interpretation such as Rahul Sharma being the all-rounder and Mohan Vishe being a payment-risk red flag, rather than just listing rows.Follow-Up Context✓ WorkedIt preserves the selected top 3 across turns and reuses the earlier result set for the payment-method follow-up without issuing a new SQL query.Result Readability✓ WorkedIt presents the answer as clearly labeled ranking tables and customer-level payment summaries, with visual risk cues for unpaid or shipped orders.SQL Generation✓ WorkedIt can generate follow-up SQL constrained to exactly the top 3 customers, using hardcoded customer IDs in the WHERE clause.
Provenance
- Observation
- 7466b188-0aed-468f-a6fd-245507d99fcc
- Evidence run
- af2abc96-3311-484b-a19d-854a2fdd2bf3
- Study
- Query Live Databases Using Plain English with AI
- Research task
- 86b9y6c99
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "askyourdatabase",
scenario: "live-database-plain-english-queries"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 5 other tools
measured on Plain English Query Handling
Basedash✓ WorkedIt correctly handled a conversational, multi-part customer question without requiring SQL, producing two rankings plus follow-up answers.Definite◐ MixedIt accepted the natural-language request but only ranked customers by total spend, so it did not fully understand the combined 'order the most and spend the most' intent.Draxlr✓ WorkedIt accepted the informal best-customers request and both follow-up questions across the three-turn conversation.FutureSmart NL2SQL Agent✓ WorkedAccepts the best-customers question in plain English and returns a ranked answer without requiring SQL from the user.Querio✓ WorkedHandles an informal conversational request and splits 'order the most' and 'spend the most' into two separate ranking dimensions instead of guessing.
This evidence is published in
From the same study (page rebuilt from a later run)
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com