abstractapi-mcp-server
Servidor MCP de API abstracta
Un servidor de Protocolo de Contexto de Modelo (MCP) que proporciona herramientas de validación de correo electrónico y teléfono mediante servicios de API Abstract. Este servidor está desarrollado con FastMCP, lo que facilita la integración de funciones de validación en aplicaciones y flujos de trabajo de IA.
Descripción general
Este servidor MCP expone tres herramientas de validación principales:
Validación de correo electrónico : validación y verificación integral de direcciones de correo electrónico
Validación de teléfono : Validación de números de teléfono para más de 190 países
Reputación de correo electrónico : análisis avanzado de la reputación del correo electrónico con información sobre seguridad
Related MCP server: revenuebase-mcp-server
Características
Validación de correo electrónico
Validación de formato
Comprobación de capacidad de entrega
Verificación de dominio
Validación SMTP
Detección de correos electrónicos desechables/de rol/generales
Puntuación de calidad
Validación telefónica
Validación de números de teléfono internacionales
Estandarización de formatos (internacionales/locales)
Identificación del país y del operador
Detección del tipo de teléfono (móvil, fijo, etc.)
Información de ubicación
Reputación del correo electrónico
Análisis integral de capacidad de entrega
Puntuación de calidad y evaluación de riesgos
Identificación del remitente y de la organización
Análisis de seguridad del dominio (DMARC, SPF)
Seguimiento del historial de violaciones de datos
Detección de fraudes y abusos
Prerrequisitos
Python 3.11+
uv (instalador rápido de paquetes de Python)
Clave API abstracta (obtenga una en abstractapi.com )
Instalación
Opción 1: Uso de rayos UV (recomendado)
Clonar el repositorio:
git clone https://github.com/avivshafir/abstractapi-mcp-server
cd abstractapi-mcp-serverCrear entorno virtual e instalar dependencias:
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install .Configurar variables de entorno:
cp .env.example .env
# Edit .env and add your Abstract API keyOpción 2: Utilizando pip tradicional
Clonar el repositorio:
git clone https://github.com/avivshafir/abstractapi-mcp-server
cd abstractapi-mcp-serverCrear un entorno virtual:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activateInstalar dependencias:
pip install -r requirements.txtConfigurar variables de entorno:
cp .env.example .env
# Edit .env and add your Abstract API keySu archivo .env debe contener:
ABSTRACT_API_KEY=your_abstract_api_key_hereUso
Ejecución del servidor MCP
El servidor se puede ejecutar en modo stdio para la integración con clientes MCP:
# With uv (if virtual environment is activated)
python server.py
# Or run directly with uv
uv run server.pyMarco FastMCP
Este servidor se creó con FastMCP , un framework de Python que simplifica el desarrollo de servidores MCP. FastMCP ofrece:
Registro automático de herramientas : las funciones decoradas con
@mcp.tool()se exponen automáticamente como herramientas MCPSeguridad de tipos : sugerencias y validación de tipos completos
Soporte asincrónico sencillo : Soporte nativo para async/await
Configuración simplificada del servidor : código repetitivo mínimo
Conceptos clave de FastMCP
from mcp.server.fastmcp import FastMCP
# Initialize the server
mcp = FastMCP("abstract_api")
# Register a tool
@mcp.tool()
async def my_tool(param: str) -> dict:
"""Tool description for AI clients"""
return {"result": param}
# Run the server
mcp.run(transport="stdio")Herramientas disponibles
1. Validación de correo electrónico ( verify_email )
Valida direcciones de correo electrónico y devuelve información completa.
Parámetros:
email(str): Dirección de correo electrónico para validar
Ejemplo de respuesta:
{
"email": "user@example.com",
"deliverability": "DELIVERABLE",
"quality_score": "0.99",
"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": true, "text": "TRUE"},
"is_smtp_valid": {"value": true, "text": "TRUE"}
}2. Validación del teléfono ( validate_phone )
Valida números de teléfono de más de 190 países.
Parámetros:
phone(str): Número de teléfono a validarcountry(str, opcional): código de país ISO para contexto
Ejemplo de respuesta:
{
"phone": "14152007986",
"valid": true,
"format": {
"international": "+14152007986",
"local": "(415) 200-7986"
},
"country": {
"code": "US",
"name": "United States",
"prefix": "+1"
},
"location": "California",
"type": "mobile",
"carrier": "T-Mobile USA, Inc."
}3. Reputación del correo electrónico ( check_email_reputation )
Proporciona un análisis completo de la reputación del correo electrónico, que incluye información de seguridad e historial de infracciones.
Parámetros:
email(str): Dirección de correo electrónico para analizar
Ejemplo de respuesta:
{
"email_address": "benjamin.richard@abstractapi.com",
"email_deliverability": {
"status": "deliverable",
"status_detail": "valid_email",
"is_format_valid": true,
"is_smtp_valid": true,
"is_mx_valid": true,
"mx_records": ["gmail-smtp-in.l.google.com", "..."]
},
"email_quality": {
"score": 0.8,
"is_free_email": false,
"is_username_suspicious": false,
"is_disposable": false,
"is_catchall": true,
"is_subaddress": false,
"is_role": false,
"is_dmarc_enforced": true,
"is_spf_strict": true,
"minimum_age": 1418
},
"email_sender": {
"first_name": "Benjamin",
"last_name": "Richard",
"email_provider_name": "Google",
"organization_name": "Abstract API",
"organization_type": "company"
},
"email_domain": {
"domain": "abstractapi.com",
"domain_age": 1418,
"is_live_site": true,
"registrar": "NAMECHEAP INC",
"date_registered": "2020-05-13",
"date_expires": "2025-05-13",
"is_risky_tld": false
},
"email_risk": {
"address_risk_status": "low",
"domain_risk_status": "low"
},
"email_breaches": {
"total_breaches": 2,
"date_first_breached": "2018-07-23T14:30:00Z",
"date_last_breached": "2019-05-24T14:30:00Z",
"breached_domains": [
{"domain": "apollo.io", "date_breached": "2018-07-23T14:30:00Z"},
{"domain": "canva.com", "date_breached": "2019-05-24T14:30:00Z"}
]
}
}Integración con clientes MCP
Agregue este servidor a su configuración de mcp:
{
"mcpServers": {
"abstract-api": {
"command": "uv",
"args": ["run", "/path/to/mcp-abstract-api/server.py"],
"env": {
"ABSTRACT_API_KEY": "your_api_key_here"
}
}
}
}Alternativamente, si prefiere utilizar el enfoque tradicional:
{
"mcpServers": {
"abstract-api": {
"command": "python",
"args": ["/path/to/mcp-abstract-api/server.py"],
"env": {
"ABSTRACT_API_KEY": "your_api_key_here"
}
}
}
}Otros clientes de MCP
Este servidor sigue el protocolo MCP estándar y se puede integrar con cualquier cliente compatible con MCP. Se comunica mediante el transporte stdio.
Manejo de errores
El servidor incluye un manejo integral de errores:
Validación de clave API : Comprueba si faltan claves API
Manejo de errores HTTP : manejo adecuado de errores de respuesta de API
Validación de entrada : verificación de tipos y validación de parámetros
Degradación elegante : mensajes de error significativos para la depuración
Límites de velocidad de la API
La API abstracta tiene diferentes límites de velocidad según su plan:
Planes gratuitos: 1 solicitud por segundo
Planes pagos: Límites de tarifas más altos disponibles
Cada llamada a la API cuenta como un crédito, independientemente de si la validación tiene éxito o falla.
Desarrollo
Estructura del proyecto
mcp-abstract-api/
├── server.py # Main MCP server implementation
├── .env # Environment variables (not in repo)
├── .env.example # Environment template
├── requirements.txt # Python dependencies (pip format)
├── uv.lock # uv lock file for reproducible builds
├── pyproject.toml # Project configuration
├── README.md # This file
└── LICENSE # MIT LicenseAgregar nuevas herramientas
Para agregar nuevas herramientas de API abstracta:
Agregue la URL del punto final de la API como una constante
Crea una nueva función decorada con
@mcp.tool()Agregue una cadena de documentación completa con descripciones de parámetros y retornos
Implementar el manejo de errores siguiendo el patrón existente
Ejemplo:
@mcp.tool()
async def new_validation_tool(param: str) -> dict[str, Any]:
"""
Description of what this tool does.
Args:
param (str): Description of parameter
Returns:
dict[str, Any]: Description of return value
"""
# Implementation here
passContribuyendo
Bifurcar el repositorio
Crear una rama de características
Realiza tus cambios
Agregue pruebas si corresponde
Enviar una solicitud de extracción
Licencia
Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.
Apoyo
Para cuestiones relacionadas con:
Este servidor MCP : Abra un problema en este repositorio
API abstracta : Póngase en contacto con el soporte de API abstracta
Marco FastMCP : consulte la documentación de FastMCP
Expresiones de gratitud
API abstracta para proporcionar los servicios de validación
FastMCP para el marco del servidor MCP
Protocolo de contexto del modelo para la especificación del protocolo
Available Tools
3 toolscheck_email_reputationA
Analyzes email reputation using Abstract API's Email Reputation service.
This function provides comprehensive email reputation analysis including deliverability,
quality scoring, sender information, domain details, risk assessment, and breach history.
It's designed to help improve delivery rates, clean email lists, and block fraudulent users.
Args:
email (str): The email address to analyze for reputation.
Returns:
dict[str, Any]: A dictionary containing comprehensive reputation analysis. The dictionary
includes the following main sections:
- "email_address" (str): The email address that was analyzed.
- "email_deliverability" (dict): Deliverability information.
- "status" (str): "deliverable", "undeliverable", or "unknown".
- "status_detail" (str): Additional detail (e.g., "valid_email", "invalid_format").
- "is_format_valid" (bool): True if email follows correct format.
- "is_smtp_valid" (bool): True if SMTP check was successful.
- "is_mx_valid" (bool): True if domain has valid MX records.
- "mx_records" (list): List of MX records for the domain.
- "email_quality" (dict): Quality assessment information.
- "score" (float): Confidence score between 0.01 and 0.99.
- "is_free_email" (bool): True if from free provider (Gmail, Yahoo, etc.).
- "is_username_suspicious" (bool): True if username appears auto-generated.
- "is_disposable" (bool): True if from disposable email provider.
- "is_catchall" (bool): True if domain accepts all emails.
- "is_subaddress" (bool): True if uses subaddressing (user+label@domain.com).
- "is_role" (bool): True if role-based address (info@, support@, etc.).
- "is_dmarc_enforced" (bool): True if strict DMARC policy enforced.
- "is_spf_strict" (bool): True if domain enforces strict SPF policy.
- "minimum_age" (int|null): Estimated age of email address in days.
- "email_sender" (dict): Sender information if available.
- "first_name" (str|null): First name associated with email.
- "last_name" (str|null): Last name associated with email.
- "email_provider_name" (str|null): Email provider name (e.g., "Google").
- "organization_name" (str|null): Organization linked to email/domain.
- "organization_type" (str|null): Type of organization (e.g., "company").
- "email_domain" (dict): Domain information.
- "domain" (str): Domain part of the email.
- "domain_age" (int|null): Age of domain in days.
- "is_live_site" (bool|null): True if domain has active website.
- "registrar" (str|null): Domain registrar name.
- "registrar_url" (str|null): Registrar website URL.
- "date_registered" (str|null): Domain registration date.
- "date_last_renewed" (str|null): Last renewal date.
- "date_expires" (str|null): Domain expiration date.
- "is_risky_tld" (bool|null): True if top-level domain is considered risky.
- "email_risk" (dict): Risk assessment.
- "address_risk_status" (str): Risk level for the email address.
- "domain_risk_status" (str): Risk level for the domain.
- "email_breaches" (dict): Data breach information.
- "total_breaches" (int|null): Number of known breaches.
- "date_first_breached" (str|null): Date of first known breach.
- "date_last_breached" (str|null): Date of most recent breach.
- "breached_domains" (list): List of breached domains with dates.
Example:
>>> await check_email_reputation("benjamin.richard@abstractapi.com")
{
"email_address": "benjamin.richard@abstractapi.com",
"email_deliverability": {
"status": "deliverable",
"status_detail": "valid_email",
"is_format_valid": true,
"is_smtp_valid": true,
"is_mx_valid": true,
"mx_records": ["gmail-smtp-in.l.google.com", ...]
},
"email_quality": {
"score": 0.8,
"is_free_email": false,
"is_username_suspicious": false,
"is_disposable": false,
"is_catchall": true,
"is_subaddress": false,
"is_role": false,
"is_dmarc_enforced": true,
"is_spf_strict": true,
"minimum_age": 1418
},
"email_sender": {
"first_name": "Benjamin",
"last_name": "Richard",
"email_provider_name": "Google",
"organization_name": "Abstract API",
"organization_type": "company"
},
"email_domain": {
"domain": "abstractapi.com",
"domain_age": 1418,
"is_live_site": true,
"registrar": "NAMECHEAP INC",
"registrar_url": "http://www.namecheap.com",
"date_registered": "2020-05-13",
"date_last_renewed": "2024-04-13",
"date_expires": "2025-05-13",
"is_risky_tld": false
},
"email_risk": {
"address_risk_status": "low",
"domain_risk_status": "low"
},
"email_breaches": {
"total_breaches": 2,
"date_first_breached": "2018-07-23T14:30:00Z",
"date_last_breached": "2019-05-24T14:30:00Z",
"breached_domains": [
{"domain": "apollo.io", "date_breached": "2018-07-23T14:30:00Z"},
{"domain": "canva.com", "date_breached": "2019-05-24T14:30:00Z"}
]
}
}
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.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing behavioral traits: it explains the comprehensive analysis scope, mentions API dependencies (Abstract API), and includes error handling details in the 'Raises' section. However, it doesn't mention rate limits, authentication requirements beyond the API key error, or whether this is a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately front-loaded with purpose and usage, but becomes overly verbose with an extremely detailed example (60+ lines) that duplicates information already implied by the return structure description. The 'Raises' section is useful but could be more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (comprehensive reputation analysis), no annotations, and no output schema, the description provides exceptional completeness: detailed purpose, parameter semantics, comprehensive return structure documentation, example output, and error handling. Nothing essential is missing for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 compensates fully by providing detailed semantics for the 'email' parameter in the Args section, explaining it's 'The email address to analyze for reputation' with clear type information and usage context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'analyzes email reputation using Abstract API's Email Reputation service' with specific verbs ('analyzes', 'provides comprehensive analysis') and distinguishes it from sibling tools (validate_phone, verify_email) by focusing on reputation analysis rather than validation or verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('designed to help improve delivery rates, clean email lists, and block fraudulent users') but doesn't explicitly state when to use this tool versus the sibling tools (validate_phone, verify_email). No explicit alternatives or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_phoneA
Validates a phone number using Abstract API's Phone Validation service.
This function checks the validity and other details of phone numbers from over 190 countries.
It returns detailed information about the phone number including format, country, location,
type, and carrier information.
Args:
phone (str): The phone number to validate and verify.
country (str, optional): The country's ISO code to indicate the phone number's country.
This helps the API append the corresponding country code to its analysis.
For example, use "US" for United States numbers.
Returns:
dict[str, Any]: A dictionary containing detailed validation results. The dictionary
includes the following keys:
- "phone" (str): The phone number submitted for validation.
- "valid" (bool): True if the phone number is valid, False otherwise.
- "format" (dict): Object containing international and local formats.
- "international" (str): International format with country code and "+" prefix.
- "local" (str): Local/national format without international formatting.
- "country" (dict): Object containing country details.
- "code" (str): Two-letter ISO 3166-1 alpha-2 country code.
- "name" (str): Name of the country where the phone number is registered.
- "prefix" (str): Country's calling code prefix.
- "location" (str): Location details (region, state/province, sometimes city).
- "type" (str): Type of phone number. Possible values: "Landline", "Mobile",
"Satellite", "Premium", "Paging", "Special", "Toll_Free", "Unknown".
- "carrier" (str): The carrier that the number is registered with.
Example:
>>> await validate_phone("14152007986")
{
"phone": "14152007986",
"valid": true,
"format": {
"international": "+14152007986",
"local": "(415) 200-7986"
},
"country": {
"code": "US",
"name": "United States",
"prefix": "+1"
},
"location": "California",
"type": "mobile",
"carrier": "T-Mobile USA, Inc."
}
>>> await validate_phone("2007986", "US")
# Will validate with US country context
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.
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | ||
| country | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure and does so comprehensively. It explains what the tool returns (detailed validation results), includes error handling information (raises section), describes the external API dependency, and provides a complete example of the return format. This goes well beyond basic functional description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, parameters, returns, example, raises) but is somewhat lengthy. Every section adds value, though some information could be more concise. The front-loaded purpose statement is clear, and the structure helps with comprehension despite the length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and 0% schema description coverage, the description provides exceptional completeness. It covers purpose, parameters, return values with detailed structure, examples, error handling, and external dependencies. The return value documentation effectively substitutes for a missing output schema, making this description highly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. It explains both parameters thoroughly: 'phone' is the number to validate, and 'country' is an optional ISO code that helps with analysis. The description includes examples showing how both parameters work, adding significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Validates a phone number using Abstract API's Phone Validation service.' It specifies the exact action (validate), resource (phone number), and service provider, distinguishing it from sibling email tools. The description goes beyond the tool name by explaining it checks validity and returns detailed information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool: for validating phone numbers from over 190 countries. It doesn't explicitly mention when not to use it or compare with alternatives, but the context is sufficiently clear given the tool's specialized function. The examples show usage patterns with and without the optional country parameter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_emailA
Validates an email address using an external email validation API of abstractapi.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it uses an external API, returns detailed validation results, and includes error handling (raises exceptions for missing API key, HTTP errors, or other issues). It covers key aspects like what the tool does and potential failures, though it could add more on rate limits or performance.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 key information. However, it includes an extensive example and detailed return value breakdown that might be verbose; some of this could be streamlined without losing clarity, but overall it remains efficient and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 output) and no annotations or output schema, the description is highly complete. It covers purpose, parameters, return values with examples, and error handling, providing all necessary context for an AI agent to understand and use the tool effectively without relying on structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides detailed parameter semantics: 'email (str): The email address to validate.' This adds clear meaning beyond the bare schema, explaining the parameter's purpose and type, which is essential given the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Validates an email address using an external email validation API of abstractapi.' It specifies the verb ('validates'), resource ('email address'), and method ('external email validation API'), distinguishing it from sibling tools like 'check_email_reputation' which likely focuses on reputation rather than validation, and 'validate_phone' which handles a different resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for email validation but does not explicitly state when to use this tool versus alternatives like 'check_email_reputation'. It mentions checking 'validity, deliverability, and other attributes', which suggests use cases, but lacks explicit guidance on when to choose this over siblings or when not to use it (e.g., for simple format checks only).
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. Dates show when Glama detected each change.
3 tool updates
- First observed
check_email_reputation - First observed
validate_phone - First observed
verify_email
TDQS
The tools have significant overlap and unclear boundaries. Both check_email_reputation and verify_email perform email validation with substantial functional overlap, making it difficult for an agent to choose between them. The phone validation tool is distinct, but the email tools appear to do similar things with different emphasis.
The naming follows a mixed pattern. Two tools use verb_noun format (check_email_reputation, verify_email) while one uses verb_noun format but with different verb style (validate_phone). The naming is readable but lacks complete consistency in verb choice across the set.
With only 3 tools, the server feels thin for an 'abstractapi-mcp-server' that presumably covers multiple Abstract API services. While the tools themselves are substantial, the count suggests limited coverage of what Abstract API likely offers, making the server feel under-scoped.
For an Abstract API server, there are significant gaps in coverage. The server only covers email and phone validation, missing other Abstract API services like IP geolocation, exchange rates, holidays, etc. Even within the covered domains, there's redundancy rather than comprehensive functionality.
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