datatomcp

[datatomcp]

The AI-native data layer for teams without a data team. Your team's data, ready for every AI agent.

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[Listed on mcpservers.org]

datatomcp is one governed place for a team's data, that every AI agent can ask: Google Sheets, CSV and Excel files, PostgreSQL and MySQL, Stripe, HubSpot and Airtable. Connect your data once: datatomcp links the datasets, drafts definitions of what each number means for a person to approve, and records every question an agent asks. Agents (Claude, ChatGPT, Cursor, n8n and others) query it over MCP or REST, and a database computes the answer, so totals are exact. News: datatomcp is now an AI-native data layer (4 Nov 2026).

• 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, versioned results. Counts, sums and averages come from DuckDB. Every result names the dataset version it came from and links to the rows behind it.
• Shared definitions. Approved definitions like paid_revenue and a glossary of your team's words. Agents use them by name, and the answer says who approved the definition.
• Audit trail. Every agent question is kept: who asked, from which agent, the query, the data version. Repeated questions are compared. Export as CSV.
• Linked datasets. Orders, customers and regions in one question, with unmatched rows counted.
• Charts and dashboards. The chart tool draws the answer in the chat (52 chart types, as an MCP Apps view). Agents make dashboards from your questions and refresh them later; dashboards never change your data.
• Workspaces. Owners, editors and viewers; each agent connection can be limited to some workspaces.
• Private Google Sheets. You pick the sheet in Google's file picker, and datatomcp can read that file only. Pro syncs within 5 minutes of an edit.
• Import report. Bad lines, numbers stored as text and mixed date formats are reported before any agent sees the data.
• Read-only data. Agents can query your data, but they can't change it or run raw SQL. The only tools that write create dashboards or send your team a note.
• Hosted in the EU. Your data is never used for training. AI features inside datatomcp (cleaning suggestions, an assistant on your own key) are off until you turn them on.

This repository holds the public documentation and client configuration. The service itself runs at app.datatomcp.com.

Server

Quick start

1. Sign up at app.datatomcp.com (free, no card).
2. Add a dataset: upload a CSV or Excel file, choose a Google Sheet, or connect PostgreSQL, MySQL, Stripe, HubSpot or Airtable. New accounts start with an example dataset.
3. Add the server to your client (below), sign in when asked, and ask a question about your data.

Tools

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/mcpcodex 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, definitions and query engine are available over REST with an API key, for apps and automations such as n8n, Make or Zapier. Reference: datatomcp.com/docs/api. OpenAPI 3.1: datatomcp.com/openapi.json.

Plans

Guides

• Connect Google Sheets to Claude
• Google Sheets MCP server
• CSV MCP server
• Analyze large CSVs with AI
• Shared definitions for AI agents
• Audit what your AI agents asked

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.