Wren AI

Ask your database, get answers

Category: Data & Analytics
Primary Subcategory: Self-Serve BI & Conversational Analytics

Integration details

Description

Wren AI brings conversational business intelligence (GenBI) into ChatGPT. Connect your company's databases, ask questions in free-form language, and get answers from live data with the SQL, visuals, and context behind them. Ask "What was revenue by region last quarter?" and Wren AI generates the SQL, runs it against your warehouse, and returns the result with a short summary or chart. Every query is built on your team's semantic model - the business definitions, metrics, and joins your data team already maintains - so ChatGPT uses the same terms and logic your analysts do instead of guessing at table names. What you can do: - Ask questions and get answers with the SQL shown - Generate SQL from a question without running it - Run a query and get results back - Summarize results or turn them into a chart - Switch between the Wren AI projects you have access to

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Data & Analytics
Primary Subcategory: Self-Serve BI & Conversational Analytics
Secondary Subcategories: None listed
Brand: Wren AI
Access: Account required
First tracked: 2026-09-29
Tool count: 8
Geography: US

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Primary Subcategory

Competing in ChatGPT Self-Serve BI & Conversational Analytics

View Category

Listed in alphabetical order

Primary Subcategory

ADECIComing soonGet notified

Alteryx InsightsComing soonGet notified

Answer ChartsComing soonGet notified

Azadea OneComing soonGet notified

Bark AIComing soonGet notified

Wren AIComing soonGet notified

Tools

8 tools agents can invoke

When the ask tool returns a "needs_clarification" result, present the question to the user, then call this tool with their answer to resume the ask and get the final result. Required fields — queryId, questionId, threadId — come from that result. Pass the chosen option value(s) in answers, and the same projectId you used for ask.
respond_clarification
This MCP server exposes multiple projects behind one endpoint. First call list_projects to choose a projectId, then call get_project_metadata with that projectId to understand the available tables and columns before asking. Pass the same projectId to every tool call. Ask a natural language question about the selected project's data (via projectId) and receive a comprehensive answer including SQL query, executed data results, and summary. Supports conversation threading for context-aware follow-up questions.
ask
Execute a read-only SQL query against the selected project's warehouse (projectId from list_projects) and receive raw data results without any AI-generated summaries or transformations.
run_sql
This MCP server exposes multiple projects behind one endpoint. First call list_projects to choose a projectId, then call get_project_metadata with that projectId to understand the available tables and columns before asking. Pass the same projectId to every tool call. Generate SQL queries from natural language questions for the selected project (via projectId), without generating summaries.
generate_sql
Execute a SQL query against the selected project (projectId from list_projects) and generate a natural language summary of the results. Useful when you already have SQL and want insights without regenerating it.
generate_summary
Generate a chart from a question and SQL query for the selected project (projectId from list_projects).
generate_chart
Get the MDL for a specific project. Requires a projectId from list_projects. MDL (Modeling Definition Language) is a declarative language used to describe tables, fields, relationships, metrics and business rules in a structured, code-like format. It turns raw database schemas into a semantic model that LLMs and tools can understand and use safely. Call this before ask/generate_sql to learn the project's tables and columns.
get_project_metadata
List the projects you are allowed to query. Use the returned id as the projectId argument for the other tools.
list_projects

Wren AI FAQ

How do I improve a ChatGPT Plugin's discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are Wren AI alternatives on ChatGPT?

As of 2026-09-29, Wren AI competes with ADECI, Alteryx Insights, Answer Charts, Azadea One, Bark AI, BlazeSQL, Bloom Profit Analytics, Book Report, ChartMogul, Cleverbridge, Coupler.io, Cube, DataAssist-IO, DATAHILL, Deepnote, Evidence Studio, Ezoic Analytics, Flourish, Fr8Labs Analytics, Fullmetrix, Hex, Hologrow, Keypup, Krewss BI, Menu Integrado, Metorik, Omni, Orion, OWOX Data Marts, Peaka, Perlucem.ai, Prism by Crossdeck, PublisherChamp, Sales Compass by Luzmo, Social Plus Agentry, Steep, Sweet Analytics, Tableau (MCP only; EOL soon), Tenzo, ThoughtSpot Spotter, ThoughtSpot SpotterCode, TitanSigma, VODER, Winnow, WisdomAI, WitCloud, XAPP Analyst in ChatGPT Self-Serve BI & Conversational Analytics, ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.

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Competing in ChatGPT Self-Serve BI & Conversational Analytics

8 tools agents can invoke