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Dataki for MongoDB

Ask MongoDB in plain English

Ask your MongoDB data questions in plain English. Export a collection to CSV and upload it, or copy it into BigQuery with Google's Dataflow template, and Dataki charts the answers.

Through a file or BigQuery A CSV export, or a copy in BigQuery. The guide takes about 5 minutes.

app.dataki.ai / chat orders.csv

How many orders did each country place last month?

Reading the schema, writing the query…
SQL 120 ms · read-only
SELECT country, COUNT(*) AS orders
FROM   orders.data
WHERE  created_at >= DATE '2026-08-01' AND created_at < DATE '2026-09-01'
GROUP BY country ORDER BY orders DESC LIMIT 5
Orders by country August · 5 rows
United StatesGermanyUnited KingdomFranceCanada 01K2K3K4K5K

The United States placed 4,218 orders in August, more than Germany and the UK together.

01Questions

What people ask MongoDB

Each one becomes a query against the MongoDB data, run read-only, with the SQL shown beside the chart so anyone who reads SQL can check it. Anything worth keeping goes on a dashboard that refreshes on its own.

  • How many orders did each country place last month?
  • Which users have the most documents in the events collection?
  • What is the average basket size by month?
  • Which records are missing an email address?

02From your assistant

Ask MongoDB from Claude, ChatGPT or Cursor

Dataki runs a hosted MCP server. Add it to your assistant once and it can read the MongoDB data in BigQuery and run read-only queries through Dataki. The assistant gets the rows back; the credentials stay encrypted in Dataki and never reach the model.

Setup for your assistant:

Claude · dataki (MCP) orders.csv

Using the orders export I uploaded to Dataki, chart orders by country for last month.

  1. list_sources · finds orders.csv
  2. get_data_context · reads the tables and what the columns mean
  3. query · runs the SQL read-only, returns the rows
  4. render_ui · draws the chart from those rows, in the chat
server URL streamable http
https://dataki.ai/api/mcp

03Setting it up

Getting MongoDB data to Dataki

A CSV export, or a copy in BigQuery. The setup guide covers it step by step.

Setup
Five minutes for a CSV export. About 20 for a BigQuery copy.
Cost
An upload is free. A Dataflow job is billed by Google for the time it runs.
Freshness
As fresh as your last export or job run.
Best for
A CSV for one question now. BigQuery for dashboards that should keep working.

Live dashboards

Every answer can become a live dashboard.

Ask in plain English. Dataki writes the SQL, runs it on your database and charts the answer. Keep it as a dashboard with its own link, and your team sees current numbers every time they open it.

app.dataki.ai/dashboards/orders-by-country orders.csv

3 views · Updated just now

Orders by country August · 5 rows
United StatesGermanyUnited KingdomFranceCanada 01K2K3K4K5K

One click turns the answer into a dashboard:

app.dataki.ai/dashboards/orders-by-country

Always current. The query runs again whenever the dashboard opens, so Friday's meeting sees Friday's numbers.

Share it
Send the link to anyone. Viewers are free on every plan.
Refine it
The SQL is always visible. Adjust it by hand or ask for a change.
Embed it
Put the same dashboard in your own product with two HTML tags.

Using Dataki with MongoDB

Can I query MongoDB in plain English?
Yes. Export a MongoDB collection to CSV and upload it, or copy it into BigQuery with Google's Dataflow template. Dataki then turns plain-English questions into SQL against that data and returns a chart, with the query shown.
How fresh is MongoDB data in Dataki?
As fresh as your latest export or copy. An uploaded CSV is a snapshot, and a BigQuery copy refreshes every time the Dataflow job runs. For a continuous copy, Google's Datastream replicates MongoDB into BigQuery too.
Can Claude, ChatGPT or Cursor query my MongoDB data?
Yes, through Dataki's hosted MCP server at https://dataki.ai/api/mcp. Connect MongoDB to Dataki, add that URL as a connector in Claude or ChatGPT (or in Cursor, VS Code or Claude Code with a Dataki key), and your assistant can read the schema and run read-only queries. It works with the results, while your credentials stay encrypted in Dataki and never reach the model.