Dataset Diff Detector MCP Server

Dataset Diff & Change Detector (Remote MCP Server)

Compare two versions of the same list of JSON rows and get exactly what changed. Hand it yesterday's price list and today's, last week's product feed and this week's, or two exports of the same CRM, name the field that identifies a row, and it tells you which rows were added, removed or changed, down to which fields moved on each one.

Built for AI agents. No install, no API key, no signup. Connect by URL and call it.

https://dataset-diff-detector.nerolabs.workers.dev/mcp

Free to use while in early access.

What it does

One call compares a before snapshot (oldRows) with an after snapshot (newRows):

1. Matches rows across the two sides on keyFields, such as sku, id or email (one field or several). Without a key it matches on full row content, so an edited row shows as one removed row plus one added row.
2. Compares every field on each matched pair, minus any ignoreFields (timestamps that always differ), or only the compareFields you name.
3. Returns one row per difference with its status, key, oldValues, newValues and changedFields, plus a summary counting added, removed, changed and unchanged rows. Turn on includeUnchanged to get the unchanged rows back too.

Comparison is exact, so nothing is quietly glossed over: "24.99" as text and 24.99 as a number count as a change, as do "Blue" and "blue ", and null becoming a value. Nested objects compare by value, whatever order their keys are in.

It is honest about messy input. The summary warns when a key value appears twice on one side (the last row is kept and the count is reported), when rows have no key field at all, and when a field you named appears in no row, which is usually a typo.

Tools

Connect

Claude Code

claude mcp add --transport http dataset-diff-detector https://dataset-diff-detector.nerolabs.workers.dev/mcp

Claude Desktop / claude.ai: Settings, Connectors, Add custom connector, paste the URL above.

Cursor, Windsurf, VS Code and other MCP clients

{
  "mcpServers": {
    "dataset-diff-detector": {
      "url": "https://dataset-diff-detector.nerolabs.workers.dev/mcp"
    }
  }
}

Example

Yesterday's and today's price list go in, with the scrape timestamp ignored:

{
  "oldRows": [
    {"sku": "A100", "title": "Blue Widget", "price": 19.99, "stock": 42, "scrapedAt": "2026-09-11T08:00:00Z"},
    {"sku": "A101", "title": "Red Widget", "price": "24.99", "stock": 0, "scrapedAt": "2026-09-11T08:00:00Z"},
    {"sku": "A102", "title": "Green Widget", "price": 15.5, "stock": 8, "scrapedAt": "2026-09-11T08:00:00Z"},
    {"sku": "A104", "title": "Purple Widget", "price": 9.99, "discount": null, "scrapedAt": "2026-09-11T08:00:00Z"},
    {"sku": "A105", "title": "Orange Widget", "price": 30, "stock": 3, "scrapedAt": "2026-09-11T08:00:00Z"}
  ],
  "newRows": [
    {"sku": "A100", "title": "Blue Widget", "price": 17.99, "stock": 30, "scrapedAt": "2026-09-12T08:00:00Z"},
    {"sku": "A101", "title": "Red Widget", "price": 24.99, "stock": 0, "scrapedAt": "2026-09-12T08:00:00Z"},
    {"sku": "A102", "title": "Green Widget", "price": 15.5, "stock": 8, "scrapedAt": "2026-09-12T08:00:00Z"},
    {"sku": "A104", "title": "Purple Widget", "price": 9.99, "discount": 0.1, "scrapedAt": "2026-09-12T08:00:00Z"},
    {"sku": "A106", "title": "Yellow Widget", "price": 12, "stock": 100, "scrapedAt": "2026-09-12T08:00:00Z"}
  ],
  "keyFields": ["sku"],
  "ignoreFields": ["scrapedAt"]
}

Five differences come out. A105 was removed, A106 was added, A100 changed price and stock, A101's price turned from text into a number, and A104 gained a discount. A102 matched exactly and is counted as unchanged:

{
  "rows": [
    {"status":"removed","key":{"sku":"A105"},"oldValues":{"sku":"A105","title":"Orange Widget","price":30,"stock":3,"scrapedAt":"2026-09-11T08:00:00Z"}},
    {"status":"changed","key":{"sku":"A100"},"oldValues":{"sku":"A100","title":"Blue Widget","price":19.99,"stock":42,"scrapedAt":"2026-09-11T08:00:00Z"},"newValues":{"sku":"A100","title":"Blue Widget","price":17.99,"stock":30,"scrapedAt":"2026-09-12T08:00:00Z"},"changedFields":["price","stock"]},
    {"status":"changed","key":{"sku":"A101"},"oldValues":{"sku":"A101","title":"Red Widget","price":"24.99","stock":0,"scrapedAt":"2026-09-11T08:00:00Z"},"newValues":{"sku":"A101","title":"Red Widget","price":24.99,"stock":0,"scrapedAt":"2026-09-12T08:00:00Z"},"changedFields":["price"]},
    {"status":"changed","key":{"sku":"A104"},"oldValues":{"sku":"A104","title":"Purple Widget","price":9.99,"discount":null,"scrapedAt":"2026-09-11T08:00:00Z"},"newValues":{"sku":"A104","title":"Purple Widget","price":9.99,"discount":0.1,"scrapedAt":"2026-09-12T08:00:00Z"},"changedFields":["discount"]},
    {"status":"added","key":{"sku":"A106"},"newValues":{"sku":"A106","title":"Yellow Widget","price":12,"stock":100,"scrapedAt":"2026-09-12T08:00:00Z"}}
  ],
  "summary": {
    "oldRecordCount": 5,
    "newRecordCount": 5,
    "counts": {"added": 1, "removed": 1, "changed": 3, "unchanged": 1},
    "differenceCount": 5,
    "returnedRowCount": 5,
    "unchangedRowsIncluded": false,
    "keyFieldsUsed": ["sku"],
    "ignoreFields": ["scrapedAt"],
    "duplicateRowsSkipped": {"old": 0, "new": 0},
    "rowsMissingKeyFields": {"old": 0, "new": 0},
    "warnings": []
  }
}

Removed rows come first, then changed rows in the order of oldRows, then added rows in the order of newRows.

Limits

Up to 500 rows per call, oldRows and newRows combined. For bigger lists, split them across several calls and keep the same key range on both sides of each call (for example SKUs A to M in one call, N to Z in the next), otherwise a row in one call looks removed while its match in another looks added. Anything larger returns a clear message rather than failing silently.

Privacy

Your rows are processed in memory and never stored. To see which tools get used, each call records the tool name, row counts, whether it succeeded, the client name your app reports, the country and a one-way hashed caller ID. Your data, your arguments and your IP address are never kept in that log.

Also available

The same engine runs on the Apify Store as Dataset Diff & Change Detector, which adds "since last run" snapshot mode (name a comparison and every run reports only what changed since the previous one, so you never supply the old side again), reads Apify datasets, CSV, Excel and JSON files and Google Sheets on either side, compares up to 100,000 rows a run, exports a CSV or Excel diff report, keeps a running change log in a named dataset, and posts each result to a webhook.

Built by Nero Labs.