Turn a CSV or Google Sheet into a remote MCP server your AI agents can query.
Website · Start free · Guides · 40-second demo
datatomcp is a hosted MCP server for your tabular data. Upload a CSV, paste a CSV link, or pick a Google Sheet. Your agent (Claude, ChatGPT, Cursor and others) can then filter, group and total the rows. The numbers are computed by a database, not estimated by the model.
- Hosted. Nothing to install or run. One URL works in every client that supports remote MCP servers.
- OAuth sign-in. No API key to paste into your agent. You can revoke access at any time.
- Exact results. Counts, sums and averages come from DuckDB. Every result says which version of the dataset it came from.
- Private Google Sheets. You pick the sheet in Google's file picker, and datatomcp can read that file only. Pro syncs it every hour.
- Import report. Bad lines, numbers stored as text and mixed date formats are reported before any agent sees the data.
- Read-only. Agents can query your data, but they can't change it or run raw SQL.
- Hosted in the EU. Your data is never sent to AI providers or used for training.
This repository holds the public documentation and client configuration. The service itself runs at app.datatomcp.com.
Server
| URL | https://app.datatomcp.com/mcp |
| Transport | Streamable HTTP |
| Auth | OAuth 2.1 (dynamic client registration and client ID metadata documents) |
| Registry name | com.datatomcp/datatomcp |
Quick start
- Sign up at app.datatomcp.com (free, no card).
- Add a dataset: upload a CSV, paste a link, or choose a Google Sheet.
- Add the server to your client (below), sign in when asked, and ask a question about your data.
Tools
| Tool | What it does |
|---|---|
list_datasets |
Your datasets, with row counts and freshness |
describe_dataset |
Columns, types, example values, data warnings and today's date |
query_rows |
Filter, sort, select columns and paginate (up to 1,000 rows per call) |
aggregate |
Group by columns; count, distinct count, sum, average, min and max |
distinct_values |
The real values in a column, so filters match exactly |
Filters support eq, neq, gt, gte, lt, lte, in, not_in, contains, starts_with, between, is_null and not_null.
Client setup
Claude (Claude.ai and Claude Desktop)
Settings → Connectors → Add custom connector. Paste https://app.datatomcp.com/mcp, name it datatomcp, connect, and allow access.
Claude Code
claude mcp add --transport http datatomcp https://app.datatomcp.com/mcp
Then run /mcp, choose datatomcp and sign in.
ChatGPT
Settings → Apps & Connectors → Advanced, and turn on developer mode. Create a connector with the URL above and OAuth authentication, then sign in.
Cursor
Settings → MCP → Add new server. Choose type HTTP and use the URL above. Cursor asks you to log in the first time.
VS Code
.vscode/mcp.json:
{
"servers": {
"datatomcp": { "type": "http", "url": "https://app.datatomcp.com/mcp" }
}
}
Windsurf
~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"datatomcp": { "serverUrl": "https://app.datatomcp.com/mcp" }
}
}
Cline
Open the MCP Servers panel → Remote Servers, name it datatomcp, and use the URL above. Or add it to cline_mcp_settings.json:
{
"mcpServers": {
"datatomcp": { "type": "streamableHttp", "url": "https://app.datatomcp.com/mcp" }
}
}
Antigravity
Customizations → MCP servers → View raw config (mcp_config.json):
{
"mcpServers": {
"datatomcp": { "serverUrl": "https://app.datatomcp.com/mcp" }
}
}
Click Refresh, then Authenticate.
OpenCode
opencode.json:
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"datatomcp": { "type": "remote", "url": "https://app.datatomcp.com/mcp" }
}
}
Codex
codex mcp add datatomcp --url https://app.datatomcp.com/mcp
codex mcp login datatomcp
Gemini CLI
gemini mcp add --transport http datatomcp https://app.datatomcp.com/mcp
Then run /mcp auth datatomcp.
Clients with local (stdio) servers only
Use mcp-remote as a bridge. It handles the sign-in:
{
"mcpServers": {
"datatomcp": { "command": "npx", "args": ["-y", "mcp-remote", "https://app.datatomcp.com/mcp"] }
}
}
Example
How many orders came from France in September, and what was the revenue?
The agent calls describe_dataset to learn the columns, then one aggregate call:
{
"dataset_id": "ds_orders",
"where": [
{ "column": "country", "op": "eq", "value": "France" },
{ "column": "order_date", "op": "between", "value": ["2026-09-01", "2026-09-30"] }
],
"metrics": [
{ "fn": "count", "as": "orders" },
{ "fn": "sum", "column": "total", "as": "revenue" }
]
}
On our 50,000-row test sheet, it answers with 277 orders and €50,279.50 in revenue. The tool result is 32 bytes, not the whole sheet.
REST API
The same datasets are also available over REST with an API key, for apps and automations such as n8n or Make. See CSV to REST API.
Plans
| Free | Pro ($12 or €12 a month) | |
|---|---|---|
| Datasets | 3 | 25 |
| Rows per dataset | 100,000 | 2,000,000 |
| Queries per day | 1,000 | 50,000 |
| Google Sheets sync | On demand | Hourly |
Guides
Support
Questions, bugs or a file that doesn't import the way you expect: [email protected], or open an issue in this repository.
License
The documentation and configuration in this repository are MIT licensed. The datatomcp service is proprietary.
