mcpField
M
mcp_email_reader

mcp_email_reader

Local
github.com/karatebossCommunication1Updated 69w ago
Repository

About mcp_email_reader

mcp_email_reader is an MCP server for reading email through compatible AI tools. It provides tools to search messages, download attachments, and list folders.

Key features

  1. 01Search emails
  2. 02Download attachments
  3. 03List folders

Use cases

  1. 01Find emails by query
  2. 02Retrieve an email attachment
  3. 03Review available mail folders

MCP support

Exposes email search, attachment download, and folder listing as tools for MCP clients.

Configuration

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

mcp.json · Universal config
{
  "mcpServers": {
    "mcp_email_reader": {
      "command": "uvx",
      "args": [
        "git+https://github.com/karateboss/mcp_email_reader@main"
      ],
      "env": {
        "IMAP_SERVER": "<IMAP_SERVER>",
        "EMAIL_ACCOUNT": "<EMAIL_ACCOUNT>",
        "EMAIL_PASSWORD_ENC": "<EMAIL_PASSWORD_ENC>",
        "EMAIL_SECRET_KEY": "<EMAIL_SECRET_KEY>"
      }
    }
  }
}
Run commanduvx 'git+https://github.com/karateboss/mcp_email_reader@main'
Environment variables to set
IMAP_SERVER
EMAIL_ACCOUNT
EMAIL_PASSWORD_ENCSecret
EMAIL_SECRET_KEYSecret

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypigit+https://github.com/karateboss/mcp_email_reader@main

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

mcp_email_reader

Model Context Protocol (MCP) server exposes several mcp tools called search_emails, download_attachment and list_folders. This has been tested on Claude Desktop and LibreChat with Ollama.

Installation

Prerequisites
Windows

  1. Install Claude Desktop (or another MCP-enabled AI tool)

  2. Install Python 3.10 or higher:

    • Download the latest Python installer from python.org
    • Run the installer, checking "Add Python to PATH"
    • Open Command Prompt and verify installation with python --version

  3. Install uv:

    • Open Command Prompt as Administrator
    • Run pip install --user uv
    • Verify installation with uv --version
macOS

  1. Install Claude Desktop (or another MCP-enabled AI tool)

  2. Install Python 3.10 or higher:

    • Using Homebrew: brew install python
    • Verify installation with python3 --version

  3. Install uv:

    • Using Homebrew: brew install uv
    • Alternatively: pip3 install --user uv
    • Verify installation with uv --version

Configuration

Add the following to your claude_desktop_config.json:

{
    "mcpServers": {
        "mcp_email_reader": {
            "command": "uvx",
            "args": [
                "--from",
                "git+https://github.com/karateboss/mcp_email_reader@main",
                "mcp_email_reader"
            ],
        "env": {
            "IMAP_SERVER": "<email server>",
            "EMAIL_ACCOUNT": "<user email account>",
            "EMAIL_PASSWORD_ENC": "<encrypted password>"
            "EMAIL_SECRET_KEY": "<secret key used to encypt password>"
        }
    }
}

Note: Clearly, the secret key can be used to decrypt the encrypted password and is therefore a potential security risk - although claude_desktop_config.json is stored locally on your laptop/PC. It has been added to the json file above since it is not possible to add a .env file when using uvx to clone the repo dynamically.

Attribution

This software package implements the ability to read emails into a MCP enabled framework and is developed by karateboss.

Contributing

We welcome contributions to improve these tools. Please submit issues and pull requests through our repository.

Support

For questions and support:

  1. Check our documentation
  2. Submit an issue in our repository

Comments

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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.