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Abhi5h3k

MCP-Email-Verify

by Abhi5h3k

Verificación de correo electrónico de MCP

Un servidor ligero de Protocolo de Contexto de Modelo (MCP) que permite a su LLM validar direcciones de correo electrónico. Esta herramienta verifica el formato del correo electrónico, la validez del dominio y la capacidad de entrega mediante la API de Validación de Correo Electrónico AbstractAPI. Ideal para integrar la validación de correo electrónico en aplicaciones de IA como Claude Desktop.


¿Qué es el Protocolo de Contexto Modelo (MCP)?

En esencia, MCP es un protocolo estandarizado diseñado para optimizar la comunicación entre modelos de IA y sistemas externos. Considérelo un lenguaje universal que permite que diferentes agentes, herramientas y servicios de IA interactúen fluidamente.

Dibujo MCP (1)


Características

  • Verificación de correo electrónico : verifique direcciones de correo electrónico en tiempo real.

  • Integración con MCP : conéctese sin problemas con LLM compatibles con MCP.

  • Fácil configuración : creado con Python y el SDK MCP para una implementación rápida.


MCP sigue una arquitectura cliente-servidor:

cliente-servidor drawio


Mira la demostración

Haga clic en la imagen a continuación para ver una demostración en video de la herramienta MCP Email Verify en acción:

Captura de pantalla 2025-03-23 115525


Related MCP server: @bounceprotect/mcp

Requisitos

  • Python : Python 3.11.0 o superior.

  • UV : 0.6.9 o superior.


Configuración

1. Clonar el repositorio

git clone https://github.com/Abhi5h3k/MCP-Email-Verify.git
cd MCP-Email-Verify

2. Instalar UV

Si no tienes UV instalado, puedes instalarlo usando los siguientes comandos:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Verificar la instalación:

uv --version

3. Configurar el entorno virtual

Crear un entorno virtual usando UV:

uv venv

Activar el entorno virtual: En Windows:

.venv\Scripts\activate

4. Instalar dependencias Instale las dependencias requeridas desde pyproject.toml usando UV:

uv install

Ejecución del servidor

  1. Configurar variables de entorno Cree un archivo .env en el directorio raíz y agregue su clave AbstractAPI :

ABSTRACT_API_KEY=your_api_key_here
  1. Ejecutar el servidor Inicie el servidor MCP:

uv run server.py

Uso

  1. Registrar el servidor con Claude Desktop Actualice el archivo claude_desktop_config.json para incluir su servidor MCP:

{
    "mcpServers": {
        "verify_mail": {
            "command": "uv",
            "args": [
                "--directory",
                "C:\\ABSOLUTE\\PATH\\TO\\MCP-Email-Verify",
                "run",
                "server.py"
            ]
        }
    }
}

imagen

  1. Reiniciar Claude Desktop Reinicie Claude Desktop para detectar la nueva herramienta.

  2. Verificar correos electrónicos Utilice indicaciones como:

Estaba intentando escribirle a Thanos a thanos@snap.io para pedirle que volviera a poner en línea mi programa favorito, pero no sé si es un correo electrónico válido. ¿Podrías comprobar si es real o solo un snap a ciegas?

Desarrollo

Formato y linting Este proyecto utiliza black e isort para formatear el código y ordenar las importaciones.

  1. Instalar dependencias de desarrollo:

     uv add black isort --dev
  2. Formatear el código:

    black .
  3. Ordenar importaciones:

  isort .

Configurar pre-commit

pre-commit install
pre-commit run --all-files

Disponible en el servidor de Smithery.ai: Verificación de correo electrónico de MCP

Artículo: Protocolo de Contexto Modelo (MCP): Una guía para principiantes sobre el futuro de la comunicación con IA

Available Tools

1 tool
verify_emailA
Validates an email address using an external email validation API.

This function checks the validity, deliverability, and other attributes of an email address.
It returns a detailed dictionary containing information about the email's format, domain,
and SMTP server.

Args:
    email (str): The email address to validate.

Returns:
    dict[str, Any]: A dictionary containing detailed validation results. The dictionary
    includes the following keys:
        - "email" (str): The email address being validated.
        - "autocorrect" (str): Suggested autocorrection if the email is invalid or malformed.
        - "deliverability" (str): The deliverability status of the email (e.g., "DELIVERABLE").
        - "quality_score" (str): A score representing the quality of the email address.
        - "is_valid_format" (dict): Whether the email is in a valid format.
            - "value" (bool): True if the format is valid, False otherwise.
            - "text" (str): A textual representation of the format validity (e.g., "TRUE").
        - "is_free_email" (dict): Whether the email is from a free email provider.
            - "value" (bool): True if the email is from a free provider, False otherwise.
            - "text" (str): A textual representation (e.g., "TRUE").
        - "is_disposable_email" (dict): Whether the email is from a disposable email service.
            - "value" (bool): True if the email is disposable, False otherwise.
            - "text" (str): A textual representation (e.g., "FALSE").
        - "is_role_email" (dict): Whether the email is a role-based email (e.g., "admin@domain.com").
            - "value" (bool): True if the email is role-based, False otherwise.
            - "text" (str): A textual representation (e.g., "FALSE").
        - "is_catchall_email" (dict): Whether the domain uses a catch-all email address.
            - "value" (bool): True if the domain is catch-all, False otherwise.
            - "text" (str): A textual representation (e.g., "FALSE").
        - "is_mx_found" (dict): Whether MX records are found for the email domain.
            - "value" (bool): True if MX records are found, False otherwise.
            - "text" (str): A textual representation (e.g., "TRUE").
        - "is_smtp_valid" (dict): Whether the SMTP server for the email domain is valid.
            - "value" (bool): True if the SMTP server is valid, False otherwise.
            - "text" (str): A textual representation (e.g., "TRUE").

Example:
    >>> await verify_email("thanos@snap.io")
    {
        "email": "thanos@snap.io",
        "autocorrect": "",
        "deliverability": "UNDELIVERABLE",
        "quality_score": "0.00",
        "is_valid_format": {
            "value": true,
            "text": "TRUE"
        },
        "is_free_email": {
            "value": false,
            "text": "FALSE"
        },
        "is_disposable_email": {
            "value": false,
            "text": "FALSE"
        },
        "is_role_email": {
            "value": false,
            "text": "FALSE"
        },
        "is_catchall_email": {
            "value": false,
            "text": "FALSE"
        },
        "is_mx_found": {
            "value": false,
            "text": "FALSE"
        },
        "is_smtp_valid": {
            "value": false,
            "text": "FALSE"
        }
    }
Raises:
    ValueError: If the API key is not found in the environment variables.
    requests.exceptions.HTTPError: If the API request fails (e.g., 4xx or 5xx error).
    Exception: For any other unexpected errors.
ParametersJSON Schema
NameRequiredDescriptionDefault
emailYes

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and delivers comprehensive behavioral disclosure. It explains the external API dependency, detailed return structure, example output, and specific error conditions (ValueError for missing API key, HTTPError for API failures, Exception for other errors). This goes well beyond basic functionality description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, Args, Returns, Example, Raises) and front-loaded with the core functionality. While comprehensive, some sections like the detailed Returns explanation could be more concise, but overall it maintains good information density with minimal redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (external API integration, detailed return structure) and absence of both annotations and output schema, the description provides exceptional completeness. It covers purpose, parameter, return value structure with detailed key explanations, concrete example, and error conditions - leaving no significant gaps for the agent to understand tool behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and only one parameter, the description fully compensates by clearly explaining the 'email' parameter as 'The email address to validate' in the Args section. However, it doesn't provide additional semantic context like format expectations or validation rules beyond what's implied by the tool's purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('validates', 'checks') and resources ('email address', 'external email validation API'). It distinguishes what the tool does by specifying it checks 'validity, deliverability, and other attributes' and returns 'detailed dictionary containing information about the email's format, domain, and SMTP server'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context through the detailed example and return value explanation, suggesting it's for email validation scenarios. However, there are no explicit guidelines on when to use this tool versus alternatives (though no sibling tools exist), prerequisites, or limitations beyond the error handling section.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedverify_email

TDQS

A4.2/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single verify_email tool has a clearly distinct purpose focused on email validation, making disambiguation trivial.

Naming Consistency5/5

With only one tool, naming consistency is inherently perfect. The tool name follows a clear verb_noun pattern (verify_email) that would be appropriate if more tools were added, though there are no other tools to compare against.

Tool Count2/5

A single tool is generally too few for most server purposes, creating a thin surface. While email verification could be a narrow domain, typical MCP servers benefit from 3-15 tools for richer functionality. This minimal tool count limits agent capabilities significantly.

Completeness3/5

For the narrow domain of email verification, the single tool provides comprehensive validation functionality. However, there are notable gaps for broader email-related operations like bulk verification, list management, or integration with email sending services that might be expected from an email-focused server.

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