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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsIn one demo reported by Ashish Sinha, 5 of 13 questions for an analyst role—38.5%—were classified as unanswerable because their correct answers required tables that role could not read. A query against such a table could return an empty result under row-level security (RLS), making “no matching records” look much like “the matching rows exist, but you cannot see them.” This is a result from one schema and question set, not a general rate for analysts or text-to-SQL systems.
What the 38.5% figure measures
Sinha’s September 23, 2026 DEV Community post defines a question as unanswerable for a caller when its correct answer requires at least one table the caller is not permitted to read. The rate is the number of such questions divided by the total number tested. It is a structural measure based on role permissions and question-to-table labels; it does not require running a language model or executing SQL.
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The reported result comes from one demo with a 42-object schema and 13 labelled questions per role. The author says the result depends on the schema, role model and question mix—not simply on which model is connected. The post does not establish independent replication or a production benchmark.
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| Role in the demo | Unanswerable questions | Reported rate |
|---|---|---|
| Analyst | 5 of 13 | 38.5% |
| Finance | 1 of 13 | 7.7% |
| HR | 4 of 13 | 30.8% |
| CFO | 0 of 13 | 0.0% |
These are the author’s reported counts for the same demo, not expected rates for people with those job titles elsewhere. The question set and access rules determine what the fractions mean. Read Ashish Sinha’s DEV Community post.
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Why a restricted table can look like missing data
In the failure path Sinha describes, the agent receives a broad schema, chooses a table the caller cannot access, and sends a query. The database remains responsible for permissions: grants or RLS can filter out rows, leaving the application with an empty result. If the application sees only that empty list, it may report “no records found” without distinguishing between two different explanations:
- No rows matched the query.
- Matching rows exist, but the caller is not allowed to see them.
An empty response alone therefore does not explain whether the data is absent or access-filtered. The demo illustrates a way that mismatch can occur; it does not show how often it occurs in production systems.
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How to measure structural unanswerability
With labelled questions
The stronger method in the post starts with a gold set that identifies which tables each question needs, alongside the tables each caller may read. For every question, compare its required tables with that caller’s denied tables. Count a question as unanswerable if the two sets intersect, then divide that count by the number of questions tested.
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Without labelled questions
When gold labels are unavailable, the post proposes generating probe questions from each restricted table’s name, hint or description, then checking whether an unscoped schema-selection step retrieves that table. This can indicate whether the table is reachable using its own vocabulary, but it is not a substitute for a rate based on representative, labelled questions. A negative result only shows that the table was not reached by those probes; it does not prove that real questions cannot lead to it.
The author also describes an early bug in which principal=None was treated as a caller with no permissions rather than as an unscoped principal. That caused the probe to report zero of five restricted tables as reachable. Sinha says the tool now has a named regression test and paired tests intended to verify that the metric can move. These are the author’s account of the implementation and tests, not an independent audit.
Why scope the schema before generating SQL?
The proposed intervention is to make schema selection caller-aware: provide the agent only the tables that caller may read, and detect when a question needs a table that is missing before SQL generation. That can surface an access-versus-availability mismatch earlier than relying on an empty query result to explain itself.
In Sinha’s demo, a full-schema path detected 0 of 10 blocked caller-question pairs before SQL, while a caller-scoped path detected 10 of 10. Those counts describe the author’s demo and its structural setup; they are not a comparison of model quality and do not predict results for a production catalogue.
| Approach | When caller access shapes the schema | What the result can establish |
|---|---|---|
| Full schema, then database filtering | After the agent has seen the schema and selected tables for a query | In the reported demo, the path did not detect blocked pairs before SQL. Database permissions still determine which rows are returned. |
| Caller-scoped schema before generation | Before the agent selects tables and generates SQL | In the reported demo, the path detected blocked pairs before SQL. The result depends on the demo’s schema, roles and questions. |
| Schema-derived vocabulary probes | During a probe of table names, hints or descriptions | Whether those probes can reach restricted tables; not a general unanswerability rate. |
Schema scoping is not an authorization control
Schema scoping is an earlier check and a way to improve what the agent knows about the caller’s accessible data. It does not replace database grants or RLS. Those controls must continue to enforce access even if an application supplies a scoped schema: schema information guides selection, while the database protects the data.
The demo supports a specific takeaway: inaccessible tables can contribute to empty-result ambiguity, and caller-aware schema selection can expose that mismatch in the described setup. It does not establish a general 38.5% failure rate, a model score, or a security guarantee.
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