- Rows
- 10
- Version
- v1
- Refreshed
- 12 d ago
4 columns
every change makes a new one
uploaded by hand
Import report
Nothing is changed silently. Fixes create a new version.
- All 10 rows imported. No issues found.
Cleaning rules
Rules a person approved. They run on every refresh and sync, and each change makes a new version.
AI suggestions are off for this workspace. Turning them on sends the column profile and 50 sample rows to Mistral (EU) when someone asks for suggestions. Workspace settings
Columns
Agents read what each column means and its unit. Change a type and the dataset rebuilds as a new version.| Column | Meaning | Type |
|---|---|---|
customer_idunique on every row | Customer identifier, unique per row iddrafted Edit | |
nameunique on every row | Name, 10 values such as Atelier No, Brightline, Elm & Oak dimensiondrafted Edit | |
segment3 values | Segment, 3 values such as Enterprise, SMB, Mid-market dimensiondrafted Edit | |
region_id3 values | Region identifier, 3 distinct iddrafted Edit |
Sample rows
The first 10 rows, exactly as agents receive them.
| customer_id | name | segment | region_id |
|---|---|---|---|
| C-001 | Atelier Nord | Enterprise | EU-W |
| C-002 | Brightline | SMB | EU-S |
| C-003 | Casa Verde | SMB | EU-S |
| C-004 | Delta Freight | Enterprise | NA |
| C-005 | Elm & Oak | SMB | EU-W |
| C-006 | Fjord Labs | Mid-market | NA |
| C-007 | Granite Co | SMB | EU-W |
| C-008 | Harbor Foods | Mid-market | NA |
| C-009 | Iris Media | Enterprise | EU-S |
| C-010 | Juniper Studio | SMB | NA |
Who can see this dataset
Everyone in this workspace sees it, in the app and through their agents.
Agent activity
Open in auditUse it
Same engine, same numbers, whichever door you use.
- Add datatomcp from the Claude, ChatGPT or Cursor directory, or add this URL as a custom connector in any agent (Claude Code, n8n…). One connector covers all your datasets.
https://app.datatomcp.com/mcp - Sign in when asked and allow access. Then ask about customers in plain language.
Full client-by-client setup on Connect.
curl https://app.datatomcp.com/v1/datasets/ds_xY7CkFBAr02f/query \
-H "Authorization: Bearer $DATATOMCP_KEY" \
-H "Content-Type: application/json" \
-d '{"limit": 10}'Create an API key in Account. Filters, metrics and paging are in the dataset docs.
Use the datatomcp dataset "customers" (id ds_xY7CkFBAr02f) to answer questions about this data. Call describe_dataset first to read the columns and today's date. Before filtering on a text column such as customer_id, call distinct_values to get the exact spelling. It is linked to regions (region_id = regions.region_id). Group or filter by their columns in the same query, for example regions.office. Use aggregate for totals, counts and averages instead of reading rows. Never sum a column marked summable: false (prices, rates). Quote numbers exactly as returned with the unit from meta.units, mention the dataset version, and pass on meta.warnings that affect the number.
Paste this into your agent's instructions so it queries this dataset the right way.