Koskamo

Dataset Cleaner & Exporter MCP

A remote MCP server that allows agents to deduplicate, flatten, and clean messy JSON rows containing emails, phones, URLs, and HTML. It supports exporting data as JSON or CSV with a maximum of 150 rows per call.

Endpoint and access

No sign-in to connect and list tools. Individual tools may still need a vendor account, credentials, or permissions; check the documented prerequisites.

Dataset Cleaner & Exporter MCP logo, by Nero LabsCommunity listingStreamable HTTPNo sign-inEndpoint checked 2026-11-07Published by Nero Labs
MCP endpoint (Streamable HTTP)
https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp
Vendor documentation

What its tools do

2 tools, grouped by the hints the server advertises. Hints are self-declared, not verified behavior.

2 of 2 have a description · 0 of 2 declare behavior hints · 1 of 1 with parameters document them all

Checks and signals

Each line is separate evidence, not a trust score.

  • Passed Endpoint answered the live check ·
  • Availability 1 of 1 checks passed in 30 days

What you can do with it

  • Deduplicate messy JSON rows
  • Flatten nested JSON structures
  • Clean emails, phones, and URLs
  • Export cleaned data as JSON or CSV

Source and maintenance

Community listingRegistry 0.1.0
Maintenance checked

Protocol and capabilities

Server
nerolabs-dataset-cleaner-exporter 0.1.0
MCP protocol negotiated in check
2025-06-18
Tools
2 advertised
Resources
Not advertised
Resource templates
Not advertised
Prompts
Not advertised
Server-advertised capabilities
{
  "tools": {
    "listChanged": false
  }
}

Server-provided instructions

Untrusted server output shown for inspection. It is not Koskamo guidance and is excluded from knowledge search.

Call clean_rows with your rows to get them back deduplicated, flattened and cleaned, with a summary of exactly what changed. Call list_capabilities first if you need the cleaning rules or limits.

Tools

2 advertised

list_capabilities
Advertised by the server during the last successful anonymous check.

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.

list_capabilities input schema
{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
clean_rows
Advertised by the server during the last successful anonymous check.

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.

clean_rows input schema
{
  "type": "object",
  "required": [
    "rows"
  ],
  "properties": {
    "rows": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "The rows to clean. Each row is a JSON object; keys may differ between rows and values may be nested."
    },
    "flatten": {
      "type": "boolean",
      "description": "Default true. Turn nested objects into flat columns. Arrays become one JSON-text cell."
    },
    "maxItems": {
      "type": "integer",
      "minimum": 0,
      "description": "Optional cap: read at most this many rows and return at most this many. 0 (default) means no cap."
    },
    "dedupKeys": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Fields that identify a duplicate, for example [\"Email\"]. Empty compares the whole row. Use the final column names: flattened (Details_founded) and renamed. Exact and case-sensitive. Rows where every key is empty count as duplicates of each other."
    },
    "dedupMode": {
      "enum": [
        "none",
        "exact",
        "normalized",
        "fuzzy"
      ],
      "type": "string",
      "description": "How duplicates are found. normalized (default) ignores case and whitespace; exact needs identical values; fuzzy also merges near-duplicates (up to 100 rows and 1000 characters of key text, so name a short field in dedupKeys); none keeps every row."
    },
    "stripHtml": {
      "type": "boolean",
      "description": "Default false. Remove HTML tags and decode common entities in text."
    },
    "cleanFields": {
      "type": "boolean",
      "description": "Default true. Normalize emails, phone numbers and URLs, detected by field name or value shape."
    },
    "coerceTypes": {
      "type": "boolean",
      "description": "Default false. Turn \"42\" into 42 and \"true\" into true. Leading-zero values like \"007\" stay text."
    },
    "emptyToNull": {
      "type": "boolean",
      "description": "Default true. Blank text becomes null."
    },
    "keepStrategy": {
      "enum": [
        "most_complete",
        "first",
        "last"
      ],
      "type": "string",
      "description": "Which duplicate survives: most_complete (default, fewest empty fields), first or last."
    },
    "outputFormat": {
      "enum": [
        "json",
        "csv"
      ],
      "type": "string",
      "description": "json (default) returns rows; csv returns the same result as CSV text in \"csv\" instead."
    },
    "columnsToKeep": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Keep only these columns (flattened names). Takes priority over columnsToRemove."
    },
    "columnRenameMap": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        {
          "type": "object",
          "additionalProperties": {
            "type": "string"
          }
        }
      ],
      "description": "Rename columns after keep/remove, as [\"oldName:newName\"] or {\"oldName\":\"newName\"}, for example {\"Details_founded\":\"founded\"}."
    },
    "columnsToRemove": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Drop these columns (flattened names). Ignored if columnsToKeep is set."
    },
    "dropEmptyFields": {
      "type": "boolean",
      "description": "Default false. Remove null and empty fields from each row."
    },
    "expandArrayField": {
      "type": "string",
      "description": "Optional. One top-level array field (for example \"offers\") to explode into one row per entry, repeating the other fields. Object entries become columns. The expanded total must stay within the row limit."
    },
    "flattenSeparator": {
      "type": "string",
      "description": "Joins nested key paths when flattening. Default \"_\"."
    },
    "similarityThreshold": {
      "type": "number",
      "maximum": 0.99,
      "minimum": 0.5,
      "description": "Fuzzy mode only. 0.5 to 0.99, default 0.9. Higher is stricter."
    }
  },
  "additionalProperties": false
}

Prompts

Not advertised

No prompt definitions available.

Resources

Not advertised

No resource definitions available.

90-day uptime

A lightweight connection check about every six hours, sampled on hosts with many listings. It shows the server answered, not that its tools work.

Too few checks for a percentage yet (1 of 1 answered).

  1. 2026-11-07: 1 of 1 checks answered

30-day availability

Aggregated from read-only remote checks; this is not a tool-execution or security guarantee.

Successful checks
1 of 1 (100%)
Average latency
302 ms
95th percentile latency
302 ms

Maintenance history

Public check and source-refresh events. Editorial and ownership records remain private.

  • · probe · passed · 302 ms · metadata changed · 2 tool(s)

Connect from your client

Each snippet registers the same endpoint; pick the client you use.

This server is operated by the named third party. Listing and endpoint checks are not an endorsement or a security audit. Review the operator's permissions, terms, and data handling before connecting. How listings are checked and corrected.

Claude Code

Run this once in a terminal. It registers the remote server. Use --scope project to write .mcp.json for the repo.
Claude Code configuration
claude mcp add --transport http dataset-cleaner-exporter-mcp https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp

Codex

Run this once. Codex stores MCP servers in ~/.codex/config.toml (or .codex/config.toml in a trusted project).
Codex configuration
codex mcp add dataset-cleaner-exporter-mcp --url https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp

Cursor

Merge this server into .cursor/mcp.json in your project, then enable dataset-cleaner-exporter-mcp in your MCP settings.
Cursor configuration
{
  "mcpServers": {
    "dataset-cleaner-exporter-mcp": {
      "url": "https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp"
    }
  }
}

Cline

Merge this into cline_mcp_settings.json (Cline → MCP Servers → Configure).
Cline configuration
{
  "mcpServers": {
    "dataset-cleaner-exporter-mcp": {
      "type": "streamableHttp",
      "url": "https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp",
      "disabled": false
    }
  }
}

VS Code

Merge this server into .vscode/mcp.json. Use MCP: List Servers to start dataset-cleaner-exporter-mcp and inspect its tools.
VS Code configuration
{
  "servers": {
    "dataset-cleaner-exporter-mcp": {
      "type": "http",
      "url": "https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp"
    }
  }
}

Gemini CLI

Run this once. It writes .gemini/settings.json in the current project; pass --scope user for ~/.gemini/settings.json.
Gemini CLI configuration
gemini mcp add --transport http dataset-cleaner-exporter-mcp https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp

Claude Desktop

Open Claude Settings > Connectors > Add custom connector, enter this server URL and enable it for the conversation. This is a manual remote-connector handoff, not a desktop JSON configuration.
Claude Desktop configuration
https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp

Windsurf

Merge this into ~/.codeium/windsurf/mcp_config.json, then refresh MCP in Cascade. Windsurf remote servers use serverUrl, not url.
Windsurf configuration
{
  "mcpServers": {
    "dataset-cleaner-exporter-mcp": {
      "serverUrl": "https://dataset-cleaner-exporter.nerolabs.workers.dev/mcp"
    }
  }
}

Let your agent find its tools

Find servers like Dataset Cleaner & Exporter MCP from your agent. Add Koskamo once to search the index for endpoints, access requirements, and client setup. Public discovery needs no key.