mcpField
Z
ZeroBounce MCP Server

ZeroBounce MCP Server

Local
github.com/avivshafirBusiness & Marketing0Updated 74w ago

About ZeroBounce MCP Server

ZeroBounce MCP Server connects MCP clients to ZeroBounce email validation. It supports single-address checks, bulk-file validation, credit lookup, and domain email-pattern searches.

Key features

  1. 01Validate individual email addresses
  2. 02Check remaining API credits
  3. 03Upload files for bulk validation
  4. 04Track, retrieve, and delete bulk files
  5. 05Search domain email patterns

Use cases

  1. 01Check an address before adding it to a mailing list
  2. 02Validate an email file in bulk
  3. 03Review validation credits and file status
  4. 04Find email patterns associated with a domain

MCP support

Exposes ZeroBounce email-validation operations as tools for MCP clients, using the official ZeroBounce Python API v2 library.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

ZeroBounce MCP Server

This is a Model Context Protocol (MCP) server for interacting with the ZeroBounce email validation service. It uses the official ZeroBounce Python API v2 library.

Features

  • Validate individual email addresses
  • Check your remaining API credits
  • Upload files for bulk validation
  • Check file status, retrieve, and delete bulk validation files
  • Search for email patterns used by a domain

Setup

System Requirements
  • Python 3.10+
  • API key from ZeroBounce
Installation
  1. Clone this repository:
git clone https://github.com/avivshafir/zerobounce-mcp-server
cd zerobounce-mcp-server
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Set up your environment variables:

    Option 1: Using a .env file (recommended)

    Create a .env file with your ZeroBounce API key:

    ZEROBOUNCE_API_KEY=your_zerobounce_api_key
    

    Option 2: Setting environment variables directly

    export ZEROBOUNCE_API_KEY="your_zerobounce_api_key"
    

You can get your API key from your ZeroBounce account dashboard.

Add the following configuration to the mcp.json file, replacing the directory path with the actual path to where you've installed this server:

{
  "mcpServers": {
    "zerobounce": {
      "command": "python",
      "args": ["/path/to/your/zerobounce-mcp-server/main.py"]
    }
  }
}
  1. Restart Cursor to apply the changes

Now you can use the ZeroBounce MCP server directly within Cursor's AI assistant.

Available Tools

The server provides the following tools:

  1. validate_email: Validate a single email address

    validate_email(email: str, ip_address: str = "") -> dict
    
  2. get_credits: Check remaining credits in your ZeroBounce account

    get_credits() -> dict
    
  3. upload_file: Upload a file for bulk validation

    upload_file(file_path: str, email_column: int, ...) -> dict
    
  4. check_file_status: Check the status of a bulk validation file

    check_file_status(file_id: str) -> dict
    
  5. get_file: Get the results of a bulk validation

    get_file(file_id: str) -> dict
    
  6. delete_file: Delete a bulk validation file

    delete_file(file_id: str) -> dict
    
  7. domain_search: Search for email patterns used by a domain

    domain_search(domain: str) -> dict
    

Dependencies

  • zerobounce-python-api-v2: The official ZeroBounce API library
  • python-dotenv: For loading environment variables from .env file
  • mcp: The Model Context Protocol library
  • asyncio: For asynchronous operations

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.