CSV reconcile
csv-reconcile · version 1.0.0 · CSV & tables · free, no key needed
Reconcile two CSV documents by key columns, reporting full population totals and bounded samples for added, removed, changed, invalid-key, and ambiguous rows.
Use when you need to: csv reconcile · reconcile csv · csv reconciliation.
Supported
- csv reconcile
- reconcile csv
- csv reconciliation
- compare csv by keys with duplicates
- csv diff with duplicate keys
- keyed csv reconciliation report
- diagnose csv differences
Not supported
- fuzzy matching
- numeric type coercion
- schema migration
- import or patch generation
- external writes
- filesystem access
- network access
Behavior
- left and right must share the same exact set of header names; header order may differ.
- keys is a non-empty unique array of at most 16 column names existing in the headers.
- Empty string in any key cell makes that row invalid-key; whitespace is literal and non-empty.
- Invalid-key rows are strictly excluded from key matching and duplicate groups.
- Tuple identity uses collision-safe exact string tuples without trimming or case folding.
- Duplicate key (>1 valid-key rows on either side) quarantines all rows of that tuple on both sides under ambiguous_keys.
- Unambiguous valid tuples are classified into added (right-only), removed (left-only), changed (non-key diffs), or unchanged.
- changed.before and changed.after contain only changed columns, with changed_columns ordered according to left CSV header order.
- Full population conservation holds: left = invalid_left + ambiguous_left + removed + changed + unchanged; right = invalid_right + ambiguous_right + added + changed + unchanged.
- Samples are bounded per category by sample_limit (default 5, 0..20); limit 0 returns all counts and empty samples.
- Row numbers are 1-based data row indices.
- Added samples follow right row order; removed and changed follow left row order; invalid samples follow their own side row order; ambiguous keys follow first appearance in left, then unseen keys in right.
- Each category truncated flag equals samples.length < total; ambiguity side-row flags compare retained row indices with that side count.
- If JSON result exceeds 192 KiB, whole sample entries are deterministically shed by removing the last entry (retaining a first-seen prefix) in fixed category priority order (invalid_right, invalid_left, ambiguous_keys, added, removed, changed) while preserving exact counts.
Input
left(string, required): max length 102400right(string, required): max length 102400keys(array of string, required): min items 1; max items 16; each min length 1sample_limit(integer, optional): min 0; max 20; default 5
Output
totals(object, required)added(object, required)removed(object, required)changed(object, required)invalid_left(object, required)invalid_right(object, required)ambiguous_keys(object, required)
Limits
- max input bytes: 245760
- max csv bytes: 102400
- max data rows: 5000
- max columns: 128
- max header name bytes: 256
- max keys: 16
- max sample limit: 20
- max output bytes: 196608
Example
Request input:
{
"left": "id,name,city\n1,Ada,London\n2,Lin,Paris\n3,Zoe,Berlin\n4,Sam,Bristol\n5,Duplicate,CityA\n5,Duplicate,CityB\n,InvalidKey,NoId\n",
"right": "city,name,id\nLondon,Ada,1\nRome,Lin,2\nOslo,Bob,6\nBristol,Sam,4\nCityA,Duplicate,5\nCityB,Duplicate,5\nNewCity,ValidKey,\n",
"keys": [
"id"
]
}
Response:
{
"result": {
"totals": {
"left_rows": 7,
"right_rows": 7,
"invalid_left": 1,
"invalid_right": 1,
"ambiguous_left": 2,
"ambiguous_right": 2,
"ambiguous_keys": 1,
"added": 1,
"removed": 1,
"changed": 1,
"unchanged": 2
},
"added": {
"total": 1,
"samples": [
{
"row": 3,
"key": [
"6"
],
"values": {
"city": "Oslo",
"name": "Bob",
"id": "6"
}
}
],
"truncated": false
},
"removed": {
"total": 1,
"samples": [
{
"row": 3,
"key": [
"3"
],
"values": {
"id": "3",
"name": "Zoe",
"city": "Berlin"
}
}
],
"truncated": false
},
"changed": {
"total": 1,
"samples": [
{
"key": [
"2"
],
"left_row": 2,
"right_row": 2,
"changed_columns": [
"city"
],
"before": {
"city": "Paris"
},
"after": {
"city": "Rome"
}
}
],
"truncated": false
},
"invalid_left": {
"total": 1,
"samples": [
{
"row": 7,
"key": [
""
],
"values": {
"id": "",
"name": "InvalidKey",
"city": "NoId"
}
}
],
"truncated": false
},
"invalid_right": {
"total": 1,
"samples": [
{
"row": 7,
"key": [
""
],
"values": {
"city": "NewCity",
"name": "ValidKey",
"id": ""
}
}
],
"truncated": false
},
"ambiguous_keys": {
"total": 1,
"samples": [
{
"key": [
"5"
],
"tuple": [
"5"
],
"left_count": 2,
"right_count": 2,
"left_rows": [
5,
6
],
"right_rows": [
5,
6
],
"left_truncated": false,
"right_truncated": false
}
],
"truncated": false
}
}
}
How to call it
MCP
Connect https://computefirst.net/mcp (setup), then call execute with:
{
"id": "csv-reconcile",
"version": "1.0.0",
"input": {
"left": "id,name,city\n1,Ada,London\n2,Lin,Paris\n3,Zoe,Berlin\n4,Sam,Bristol\n5,Duplicate,CityA\n5,Duplicate,CityB\n,InvalidKey,NoId\n",
"right": "city,name,id\nLondon,Ada,1\nRome,Lin,2\nOslo,Bob,6\nBristol,Sam,4\nCityA,Duplicate,5\nCityB,Duplicate,5\nNewCity,ValidKey,\n",
"keys": [
"id"
]
}
}
HTTP (no key)
curl -X POST https://computefirst.net/v1/tools/csv-reconcile/versions/1.0.0/execute \
-H "Content-Type: application/json" \
-d '{"left":"id,name,city\n1,Ada,London\n2,Lin,Paris\n3,Zoe,Berlin\n4,Sam,Bristol\n5,Duplicate,CityA\n5,Duplicate,CityB\n,InvalidKey,NoId\n","right":"city,name,id\nLondon,Ada,1\nRome,Lin,2\nOslo,Bob,6\nBristol,Sam,4\nCityA,Duplicate,5\nCityB,Duplicate,5\nNewCity,ValidKey,\n","keys":["id"]}'
The machine-readable contract is at /v1/tools/csv-reconcile/versions/1.0.0.
CLI
node cli.mjs run csv-reconcile 1.0.0 --input input.json --base-url https://computefirst.net
Get the client at /clients/cli/.
Related tools
- CSV row diff: Compare two CSV documents with identical headers by unique key tuples and report added, removed, and changed rows.
- CSV dedupe: Remove duplicate CSV data rows while preserving the first occurrence.
- CSV anti join: Keep left CSV rows whose exact key tuples do not appear in the right document.
- CSV concat: Concatenate CSV documents that share identical headers, preserving document and row order.
- CSV fill defaults: Replace empty cells in named CSV columns with exact default strings.
- CSV filter equals: Keep CSV data rows whose named column equals an exact string.