People who already live in an AI assistant on a desktop, Claude, Cursor, ChatGPT, and have hit the wall where it can read their work but not change it. Founders, operators and developers who keep real work in Google Workspace and Jira, usually across more than one account, and who want the assistant to finish a task rather than hand back something to copy and paste.
It's not a consumer mobile product. Getting value from it means connecting a real Workspace account and pointing an AI client at the endpoint
Most AI connectors read your files but can't change them. Claude's built-in Google Drive connector will open the spreadsheet you've kept for years and summarise it, but there's no appending a row or updating a cell in an existing Sheet, and its Gmail connector drafts mail without being able to send it.
DataToRAG is an MCP gateway that closes that gap. 76 hand-built tools across Google Workspace, Jira and Confluence, with real write verbs, behind a single URL you paste into Claude, Cursor or ChatGPT. It creates the sheet, asks you before it writes, and appends the rows โ on the same account, from the same question.
Because you sign into it rather than build on it. Automation platforms hand you a builder and expect you to wire up auth, tokens and per-connector plumbing; DataToRAG is one Google sign-in and one endpoint.
What sits behind that endpoint is the part worth comparing: 54 hand-built tools across all 8 Google Workspace services with genuine write verbs, plus 22 for Jira and Confluence. Multi-account is native. Connect work and personal, run a free/busy lookup across both, and write to whichever you choose. Every write pauses for approval before it runs. It's MIT-licensed, so you can self-host the whole thing with Docker Compose, and it's a Google-verified app with CASA Tier 2 security approval.
Built after repeatedly hitting the same wall using AI assistants for real work, the assistant could see everything in Workspace and change none of it, so every task ended in copy-paste. The gateway started as internal tooling to make an assistant actually finish the job, and is still run that way day to day.
Next.js 16 and React 19 with TypeScript and Tailwind CSS v4. Postgres via Drizzle ORM, hosted on Neon. The gateway speaks the Model Context Protocol using the official MCP SDK, with the agent layer built on Mastra and the Vercel AI SDK against Anthropic models. Google Workspace and Atlassian REST APIs behind OAuth. Packaged with Docker Compose, with PostHog for analytics and Stripe for billing
We have collected here some useful links to help you find out if DataToRAG is good.
Check the traffic stats of DataToRAG on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of DataToRAG on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of DataToRAG's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of DataToRAG on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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