live wire
▸Snowflake’s Cortex Agent quickstart routes metrics and transcripts with prompt rules—but documents no authorization boundarySnowflake▸Oracle Select AI generates SQL/PGQ for property graphs—but errors if one AI profile mixes graphs with tables or viewsOracle▸Domo’s data team standardizes MCP builds with shared scaffolding; beta toolkits inherit user permissionsDomo▸Databricks proposes five outcome signals for agentic BI—and says dashboard counts and license usage miss decision qualityDatabricks▸One vibe-coded database app needed 56+ human test hours and three repair rounds despite AI Studio’s verification claimsMancas & Mancas / arXiv▸BigQuery AI.IF’s default cost mode trains a local model for most rows—and drops row-error thresholdsGoogle Cloud▸AWS’s multi-tenant data-agent pattern gives the broker Lambda no data permissions; Cognito roles carry Lake Formation scopeAWS Big Data Blog▸Amazon OpenSearch’s optimized engine adds nested agent traces, but AWS reserves it for log analytics—not vector searchAWS▸Two interpretation-based provenance semantics cannot express SQL bag difference, new theorem showsKolaitis / arXiv▸Databricks Feature Views reuse one definition for training and live scoring, but online lookup still returns latest available stateDatabricks▸Databricks Genie’s Slack and Teams previews default channel answers to public, even when viewers lack source-data accessDatabricks▸Snowflake’s two-tool GraphRAG agent beat a seven-tool graph setup 70% to 60% on 22 ontology questionsSnowflake Engineering▸Semantix opens an Apache-2.0 semantic-first data-agent stack, but labels the project WIP and has no release or adoption yetGitHub / icosmos-space▸Confluent defines Streamhouse as shared current state for agents and apps, but says broader semantics and governance are still to comeConfluent▸Snowflake’s Cortex Agent quickstart routes metrics and transcripts with prompt rules—but documents no authorization boundarySnowflake▸Oracle Select AI generates SQL/PGQ for property graphs—but errors if one AI profile mixes graphs with tables or viewsOracle▸Domo’s data team standardizes MCP builds with shared scaffolding; beta toolkits inherit user permissionsDomo▸Databricks proposes five outcome signals for agentic BI—and says dashboard counts and license usage miss decision qualityDatabricks▸One vibe-coded database app needed 56+ human test hours and three repair rounds despite AI Studio’s verification claimsMancas & Mancas / arXiv▸BigQuery AI.IF’s default cost mode trains a local model for most rows—and drops row-error thresholdsGoogle Cloud▸AWS’s multi-tenant data-agent pattern gives the broker Lambda no data permissions; Cognito roles carry Lake Formation scopeAWS Big Data Blog▸Amazon OpenSearch’s optimized engine adds nested agent traces, but AWS reserves it for log analytics—not vector searchAWS▸Two interpretation-based provenance semantics cannot express SQL bag difference, new theorem showsKolaitis / arXiv▸Databricks Feature Views reuse one definition for training and live scoring, but online lookup still returns latest available stateDatabricks▸Databricks Genie’s Slack and Teams previews default channel answers to public, even when viewers lack source-data accessDatabricks▸Snowflake’s two-tool GraphRAG agent beat a seven-tool graph setup 70% to 60% on 22 ontology questionsSnowflake Engineering▸Semantix opens an Apache-2.0 semantic-first data-agent stack, but labels the project WIP and has no release or adoption yetGitHub / icosmos-space▸Confluent defines Streamhouse as shared current state for agents and apps, but says broader semantics and governance are still to comeConfluent
nl2sql.ai
releaseNEW TOOL

Showboard gives coding agents a read-only SQL path to dashboards—but its benchmark is only a starting point

The new MIT-licensed MCP server turns PostgreSQL, SQLite and spreadsheet queries into inspectable boards, while its cost and speed claims remain narrowly author-tested.

By The Tools Desk· Oct 24, 2026the quick take — two AI hosts go live when you do

A dashboard tool, not another chat interface

Showboard is a new MIT-licensed MCP server that lets coding agents produce dashboards from PostgreSQL, SQLite, Excel and CSV data. Instead of asking the model to write a bespoke analysis script and HTML page, the agent sends SQL and a layout through MCP; Showboard runs the query and renders charts, tables and headline metrics in a browser, an application or a terminal pane. The repository names Claude Code, Codex, Cursor and Gemini CLI among its supported clients. (Showboard README)

That architecture makes the project notable for the NL2SQL directory. Showboard does not replace the model that writes SQL. It narrows what happens after generation: PostgreSQL queries run inside a read-only transaction with row and time limits, while SQLite files and spreadsheets are opened read-only. Every rendered element can expose its backing query, giving users a direct path from a number on screen to the SQL that produced it. (Showboard README)

The first public release line is already packaged beyond source code. Version 0.4.1, published October 22, includes a 2.9 MB MCPB extension for the Claude desktop app and adjusts the Claude Code plugin’s startup behavior for Anthropic’s plugin directory. (Showboard 0.4.1 release)

The benchmark is useful—and narrow

The project’s author reports a 24-answer comparison using Claude Code with Opus 5.5 on one spreadsheet dataset. Across those runs, Showboard cost $1.78 versus $4.77 for the agent working alone, used 36,659 versus 128,929 output tokens, and took 7.5 versus 24.3 minutes. The repository includes the questions and rerun instructions, which is better evidence than an unsupported efficiency claim. (Showboard benchmark)

But those numbers should not be generalized into a universal 2.7× saving. The project explicitly says the comparison covers two runs on one dataset, and other agents have not yet been measured end to end. It also notes that the standalone Claude pages were more individually designed than Showboard’s standardized boards. (Showboard README)

The practical reason to test Showboard is therefore not the headline multiplier. It is the operating boundary: query execution is constrained, the visualization path is reusable, and the SQL remains inspectable. Teams evaluating it should rerun the supplied benchmark on their own schemas, models and recurring questions, then separately check query correctness and permission behavior. The current evidence shows a credible new tool with a reproducible test harness—not an independently validated performance result.

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Filed by The Tools Desk. Corrections: desk@nl2sql.ai · Our standards →

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