Query your CSVs and Google Sheets from any AI agent. Upload a file or pick a sheet, then filter, group and total the rows with exact results. OAuth sign-in, read-only, hosted in the EU.
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Scored across 5 tools
Each tool targets a distinct part of the data-querying workflow: discovery (list_datasets), schema inspection (describe_dataset), value exploration (distinct_values), row retrieval (query_rows), and summary metrics (aggregate). The only slight overlap is that distinct_values and aggregate can both produce counts per category, but their descriptions clearly differentiate the intended use cases.
Three of five tools follow a clear verb_noun pattern (list_datasets, describe_dataset, query_rows). aggregate is a bare verb and distinct_values is an adjective_noun phrase, so the convention is mostly consistent but not uniform; all names use snake_case.
Five tools are well-scoped for a read-only data query server, covering metadata listing, schema description, value exploration, row retrieval, and aggregation without redundancy.
The set covers the full read-only lifecycle: discovering datasets, understanding their schema and quality, exploring values, retrieving rows, and computing aggregates. No major gaps for a query/analytics MCP; write operations are out of scope.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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