Can I query PlanetScale in plain English instead of writing SQL?

Yes. Dataki reads your PlanetScale schema, turns a plain-English question into a query, runs it against PlanetScale and returns a chart. The generated query is shown with every result, so you can verify or edit it.

Is there a hosted PlanetScale MCP server?

Dataki is one. Its remote MCP server reaches PlanetScale through a read-only connection you set up once in Dataki, so nothing is installed on your machine and no connection string goes into an assistant's config. It offers tools to list sources, read the schema and its documented meaning, run read-only SQL, draw charts in the conversation and save dashboards. Every query is logged with the key that ran it.

How do I build a dashboard from PlanetScale data without an ETL pipeline?

Connect PlanetScale to Dataki with a read-only connection, ask your question, and keep the answer as a dashboard widget. Dataki queries PlanetScale directly at read time, so there is no pipeline to build, no warehouse to sync into and no copy of your data to keep current.

Can Claude, ChatGPT or Cursor query my PlanetScale data?

Yes, through Dataki's hosted MCP server at https://dataki.ai/api/mcp. Connect PlanetScale 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 the assistant can read the schema and run read-only queries. It gets the rows back; the credentials stay encrypted in Dataki and are never handed to the model.

Dataki for PlanetScale

Ask PlanetScale in plain English

Connect PlanetScale to Dataki with a read-only password and ask your database questions in plain English. Dataki connects over TLS and keeps the answers as live dashboards.

Connects directly A read-only password, over TLS.

Connect PlanetScale Read the setup guide

app.dataki.ai / chat PlanetScale · main

What were orders worth each day this past week?

Reading the schema, writing the query…

SELECT DATE(created_at) AS day, SUM(total) AS revenue
FROM   orders
WHERE  created_at >= CURDATE() - INTERVAL 6 DAY
GROUP BY day ORDER BY day;

Saturday was the busiest day, with $31k in orders. The week came to $167k.

01Questions

What people ask PlanetScale

Each one becomes a query against PlanetScale, 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 came in each day this week, and what were they worth?
Which merchants had the most failed payments last month?
What is the average time between sign-up and first order?
Which SKUs sold more this month than last?

02From your assistant

Ask PlanetScale from Claude, ChatGPT or Cursor

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

Setup for your assistant:

Claude · dataki (MCP) PlanetScale · main

Chart daily orders and their value for the last 14 days from PlanetScale.

list_sources · finds PlanetScale · main
get_data_context · reads the tables and what the columns mean
query · runs the SQL read-only, returns the rows
render_ui · draws the chart from those rows, in the chat

server URL streamable http

https://dataki.ai/api/mcp

03Setting it up

Connecting PlanetScale

A read-only password, over TLS. The setup guide covers it step by step.

Setup: About 5 minutes.
Connects from: 34.89.253.13, one fixed address to allow.
Encryption: TLS whenever the server offers it. The server certificate is not verified, and client certificates are not supported.
What it reads: Tables and views in the database you name.

Read the Hosted MySQL guide Open Dataki

The answer is still there tomorrow.

Ask in plain language. Dataki writes the SQL, runs it against your warehouse and draws the chart. Anything worth keeping becomes a dashboard with a link — not a message that scrolls away.

app.dataki.ai / chat

Which channels actually brought in revenue last quarter?

Queried bigquery · payments · 1.2 GB scanned

SELECT channel,
       SUM(amount) AS revenue
FROM   payments
WHERE  created_at >= DATE_TRUNC(CURRENT_DATE(), QUARTER)
GROUP BY channel
ORDER BY revenue DESC

Revenue by channel

Q3 · 5 rows

Organic search: $412,900
Partner referral: $268,400
Email: $151,200
Paid social: $96,700
Direct: $61,300

One click later, that chart is a dashboard of its own:

app.dataki.ai/d/revenue-by-channel

Live, not a screenshot. It re-runs the query on a schedule, so the number your team reads on Friday is Friday's number.

Share the link with anyone. Viewers are free on every plan.
Edit the SQL by hand whenever the model gets the logic almost right.
Drop the same dashboard into your own product with one line of HTML.

Start free See how it works

Using Dataki with PlanetScale

Can I query PlanetScale in plain English instead of writing SQL?: Yes. Dataki reads your PlanetScale schema, turns a plain-English question into a query, runs it against PlanetScale and returns a chart. The generated query is shown with every result, so you can verify or edit it.
Is there a hosted PlanetScale MCP server?: Dataki is one. Its remote MCP server reaches PlanetScale through a read-only connection you set up once in Dataki, so nothing is installed on your machine and no connection string goes into an assistant's config. It offers tools to list sources, read the schema and its documented meaning, run read-only SQL, draw charts in the conversation and save dashboards. Every query is logged with the key that ran it.
How do I build a dashboard from PlanetScale data without an ETL pipeline?: Connect PlanetScale to Dataki with a read-only connection, ask your question, and keep the answer as a dashboard widget. Dataki queries PlanetScale directly at read time, so there is no pipeline to build, no warehouse to sync into and no copy of your data to keep current.
Can Claude, ChatGPT or Cursor query my PlanetScale data?: Yes, through Dataki's hosted MCP server at https://dataki.ai/api/mcp. Connect PlanetScale 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 the assistant can read the schema and run read-only queries. It gets the rows back; the credentials stay encrypted in Dataki and are never handed to the model.

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