It accepted the informal best-customers request and both follow-up questions across the three-turn conversation.
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 resolved the vague phrase best customers as highest order count plus highest total spend instead of asking for a definition.Business Insight⚠ StruggledIt produced query descriptions, but no after-the-table narrative that distilled the result into a takeaway like which top customer had unpaid orders.Follow-Up Context✓ WorkedIt kept the top 3 from that list context intact across both follow-ups and continued analyzing the same three customers.Result Readability⚠ StruggledAll three best-customer result tables keep customer_id as the lead column, so the output stays UUID-heavy and hard to scan.SQL Generation✓ WorkedAcross the three turns it generated production-grade SQL, including ranking logic, unpaid-order checks, and payment-method aggregation with CTEs, ROW_NUMBER, COALESCE, and LEFT JOIN.
Provenance
- Observation
- 32f69b4e-ce68-451c-ae6d-098434171c49
- 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: "draxlr",
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
AskYourDatabase✓ WorkedIt can interpret informal ranking language like 'the ones who order the most and spend the most' and launch the analysis directly.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.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