io.github.Nero-EnginePublished by GitHub account Nero-Enginev0.1.0Updated Oct 25, 2026
Dedupe, flatten and clean messy JSON rows (emails, phones, URLs, HTML) in one call, as JSON or CSV.
A remote MCP server that deduplicates, flattens and cleans messy JSON rows into spreadsheet-ready JSON or CSV in one call.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/io.github.Nero-Engine%2Fdataset-cleaner-exporter/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Add this to your client's mcpServers config.
{
"mcpServers": {
"dataset-cleaner-exporter": {
"type": "http",
"url": "https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp"
}
}
}Deduplicate, flatten and clean messy JSON rows in a single tool call. Hand it a list of rows from a scraper, a CRM export or an API, and it hands back clean, spreadsheet-ready rows (or CSV text) plus an exact account of what was merged and why.
Built for AI agents. No install, no API key, no signup. Connect by URL and call it.
Free to use while in early access.
One call runs a fixed pipeline, in this order:
offers or variants, into one row per entry, repeating the other fields.address.city becomes address_city. Arrays stay as one JSON-text cell.+), lowercase URL hosts and drop the trailing slash, and optionally strip HTML and turn "42" and "true" into real numbers and booleans. Blank text becomes null.Deterministic, no AI in the loop, nothing guessed. A value it cannot confidently read, such as "not-an-email" in an email column, is left exactly as it was. The summary tells you how many rows came in, how many duplicates were removed, how many rows went out, how full every column is, and warns you when something needs a look, for example rows with an empty dedup key, which are kept as they are rather than merged with each other.
Claude Code
Claude Desktop / claude.ai: Settings, Connectors, Add custom connector, paste the URL above.
Cursor, Windsurf, VS Code and other MCP clients
Five messy lead rows go in, two of them duplicates with different casing and whitespace:
Three clean rows come out. Each duplicate pair kept its more complete row:
Add "outputFormat": "csv" to get the same result back as CSV text, ready to save as a file.
dedupKeys, such as a company name. Beyond that it falls back to normalized matching and says so in the warnings.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.
The same engine runs on the Apify Store as Dataset Cleaner & Exporter, which also reads Apify datasets, CSV, Excel and JSON files by URL and Google Sheets, handles up to 100,000 records per run, exports real downloadable CSV and Excel files, appends to a named dataset that accumulates across scheduled runs, and pushes the result to a webhook when a run finishes.
Built by Nero Labs.
Source: README.md at commit 91cfd85
Returns the exact cleaning rules (how emails, phone numbers and URLs are detected and normalized), the dedup modes and keep strategies, the order the steps run in, and the maximum rows per call. Call this first if you are unsure how a field will be treated. Free, processes no data.
Takes no parameters.
Deduplicates, flattens and cleans a list of JSON rows in one call and returns spreadsheet-ready rows (or CSV text) plus a summary with exact counts: rows in, rows added by expansion, duplicates removed, rows dropped by maxItems, rows out, the final column list, per-column fill rates and warnings. Steps, in order: optionally explode one array field into one row per entry; flatten nested objects into columns (address.city becomes address_city); trim text; lowercase valid emails; reduce phone numbers to digits with any leading +; lowercase URL hosts and drop the trailing slash; optionally strip HTML and turn numeric or true/false text into numbers and booleans; blank text becomes null; keep, remove or rename columns; then remove duplicates (normalized by default, comparing the whole row unless dedupKeys is set) keeping the most complete row. Deterministic, no AI, nothing guessed. Use it on scraped leads, CRM exports or API results before loading them anywhere. There is a row limit per call (see list_capabilities); split bigger lists across several calls.
rowsrequiredflattenmaxItemsdedupKeysdedupModestripHtmlcleanFieldscoerceTypesemptyToNullkeepStrategyoutputFormatcolumnsToKeep+6 more90% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
Declare a license in the repository or the registry entry.
https://dataset-cleaner-exporter.nerolabs.workers.dev/mcpclaude mcp add --transport http dataset-cleaner-exporter https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp{ "mcpServers": { "dataset-cleaner-exporter": { "url": "https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp" } }}{ "rows": [ {"company": " Acme Corp ", "email": "[email protected]", "phone": "+1 (555) 123-4567", "website": "HTTPS://Acme.com/", "address": {"city": "Austin", "state": "TX"}}, {"company": "Acme Corp", "email": "[email protected] ", "phone": "555.123.4567", "website": "https://acme.com", "address": {"city": "Austin", "state": ""}}, {"company": "Globex Inc", "email": " [email protected]", "phone": "+1 555 987 6543", "website": "https://globex.io/", "address": {"city": "Denver", "state": "CO"}, "notes": "<b>Hot</b> lead & ready"}, {"company": "globex inc", "email": "[email protected]", "phone": "", "website": "", "address": {"city": "Denver", "state": "CO"}}, {"company": "Initech", "email": "[email protected]", "phone": "(555) 010-0199", "website": "http://www.INITECH.com/about/", "address": {"city": "Dallas", "state": "TX"}} ], "dedupKeys": ["email"], "stripHtml": true}{ "rows": [ {"company": "Acme Corp", "email": "[email protected]", "phone": "+15551234567", "website": "https://acme.com", "address_city": "Austin", "address_state": "TX"}, {"company": "Globex Inc", "email": "[email protected]", "phone": "+15559876543", "website": "https://globex.io", "address_city": "Denver", "address_state": "CO", "notes": "Hot lead & ready"}, {"company": "Initech", "email": "[email protected]", "phone": "5550100199", "website": "http://www.initech.com/about", "address_city": "Dallas", "address_state": "TX"} ], "summary": { "inputRowCount": 5, "rowsAddedByExpansion": 0, "duplicatesRemoved": 2, "droppedByMaxItems": 0, "outputRowCount": 3, "dedupModeUsed": "normalized", "columns": ["company", "email", "phone", "website", "address_city", "address_state", "notes"], "fillRates": {"company": 1, "email": 1, "phone": 1, "website": 1, "address_city": 1, "address_state": 1, "notes": 0.333}, "warnings": [] }}