datatomcp

Turn a CSV or Google Sheet into a remote MCP server your AI agents can query.

Website · Start free · Guides · Demo video

---

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

1. Sign up at app.datatomcp.com (free, no card).
2. Add a dataset: upload a CSV, paste a link, or choose a Google Sheet.
3. 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

• Connect Google Sheets to Claude
• Google Sheets MCP server
• CSV MCP server
• Analyze large CSVs with AI

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.