The answer is not the only output that matters when an AI agent queries a database.
The system also needs evidence.
What data was touched?
Which scope was applied?
How many rows came back?
Was the result truncated?
Was the schema context current?
Did the agent summarize raw rows or approved aggregates?
If that information disappears before the final response, the answer becomes hard to trust and harder to debug.
That is why AI database tools need result contracts.
Raw rows are not enough
A database tool can return rows and let the model summarize them.
That works for demos.
In production, raw rows alone leave too much ambiguity:
- Was a row limit applied?
- Did the query time out?
- Were some columns redacted?
- Was tenant scope enforced?
- Which metric definition was used?
- Was the result fresh enough for the question?
The model may produce a confident summary while important caveats are lost.
What a useful result contract includes
A database MCP tool should usually return metadata alongside the data:
- tool name and version
- approved scope
- tables, views, or APIs touched
- query class: lookup, aggregate, broad read, search
- row count returned and row limit applied
- execution time and timeout status
- freshness timestamp
- redaction or masking status
- warnings the model should preserve in the final answer
This makes the final response less magical and more inspectable.
Full article: Tool result contracts for AI database agents
Conexor helps teams connect PostgreSQL, MySQL, SQL Server, REST APIs, and other sources to MCP-compatible AI clients.
For natural-language database access, the job is not just to return rows.
It is to return answers with enough context, scope, and evidence that people can trust how the answer was produced.
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