How to Give Claude Data Cleaning Tools with MCP

The Model Context Protocol (MCP) lets an AI client call external tools. Zapinner hosts an MCP server, so you can give Claude — or Cursor, or your own agent — reliable data-cleaning tools in a few lines of config. Nothing to deploy.

1. Get an API key

Create a key in your Zapinner dashboard. The MCP server authenticates with the same key as the REST API, and usage is metered to your account identically.

2. Point your MCP client at the hosted endpoint

{
  "mcpServers": {
    "zapinner": {
      "url": "https://zapinner.com/mcp",
      "headers": { "Authorization": "Bearer zap_live_..." }
    }
  }
}

3. Use it in conversation

Once connected, the client can call Zapinner tools when the task calls for them. You describe the goal in plain language; the agent picks the tool.

You:   "Here's a messy lead list — clean it and remove duplicates."
Claude: calls zapinner.repair { records: [...], options: { dedupe: true } }
        -> clean records + a summary of every change
Claude: "Repaired 200 leads: 41 formatting fixes, 12 duplicates merged."

Why this beats prompting

The model decides when to clean; Zapinner decides how, deterministically. You get reproducible results with the same guarantees as the REST API — per-record metering, structured errors, and an audit of every change — instead of logic the model reinvents each run.

For the full tool list and setup details, see the MCP documentation.