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trykittai-mcp-server

Name: trykittai-mcp-server
Author: avivshafir

by avivshafir

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Servidor mcp de TryKitt.ai

Un servidor FastMCP (Protocolo de Contexto de Modelo) que proporciona funciones de verificación y búsqueda de correo electrónico mediante la API TryKitt.ai . Este servidor permite a los asistentes de IA encontrar y verificar direcciones de correo electrónico B2B con alta precisión y bajas tasas de rebote.

Características

Verificación de correo electrónico : verifique direcciones de correo electrónico con SMTP avanzado y verificación general
Búsqueda de correo electrónico : encuentre direcciones de correo electrónico de personas usando su nombre y dominio de la empresa
Gestión de trabajos : seguimiento y control de la verificación de correo electrónico/búsqueda de trabajos
Procesamiento en tiempo real : obtenga resultados inmediatos para las operaciones de correo electrónico
Alta precisión : aprovecha los algoritmos de verificación avanzados de TryKitt.ai con una tasa de rebote <0,1 %

Related MCP server: ones-wiki-mcp-server

Instalación

Clonar este repositorio:

git clone https://github.com/avivshafir/trykittai-mcp-server
cd trykittai-mcp-server

Inicializar un nuevo entorno de Python con uv:

# Initialize a new uv project (if starting fresh)
uv init

# Or create a virtual environment
uv venv

# Activate the virtual environment
source .venv/bin/activate  # On macOS/Linux

Instalar dependencias usando uv:

# Using uv (recommended)
uv sync

Configuración

Obtén tu clave API de TryKitt.ai:
- Visita TryKitt.ai
- Regístrese para obtener una cuenta
- Vaya a la configuración de su API para obtener su clave API
Establezca su clave API como una variable de entorno:

export TRYKITT_API_KEY="your_api_key_here"

O cree un archivo .env en la raíz del proyecto:

TRYKITT_API_KEY=your_api_key_here

Uso

Ejecución del servidor

Inicie el servidor FastMCP:

python server.py

El servidor se iniciará y estará disponible para conexiones MCP.

Agregar a clientes MCP

Para utilizar este servidor con clientes compatibles con MCP, deberá configurar el cliente para que se conecte a este servidor.

Escritorio de Claude

Agregue la siguiente configuración a su archivo de configuración de Claude Desktop:

macOS : ~/Library/Application Support/Claude/claude_desktop_config.json Windows : %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "trykittai": {
      "command": "python",
      "args": ["/path/to/your/trykittai-mcp-server/server.py"],
      "env": {
        "TRYKITT_API_KEY": "your_api_key_here"
      }
    }
  }
}

Otros clientes de MCP

Para otros clientes compatibles con MCP, configúrelos para que se conecten a:

Comando : python
Argumentos : ["/path/to/your/trykittai-mcp-server/server.py"]
Variables de entorno : TRYKITT_API_KEY=your_api_key_here

Uso con uv

Si está utilizando uv, también puede ejecutar el servidor con:

{
  "mcpServers": {
    "trykittai": {
      "command": "uv",
      "args": ["run", "python", "server.py"],
      "cwd": "/path/to/your/trykittai-mcp-server",
      "env": {
        "TRYKITT_API_KEY": "your_api_key_here"
      }
    }
  }
}

Nota : reemplace /path/to/your/trykittai-mcp-server con la ruta absoluta real a su directorio de proyecto y your_api_key_here con su clave API de TryKitt.ai real.

Herramientas disponibles

1. Verificación de correo electrónico ( `verify_email_send` )

Verificar si una dirección de correo electrónico es válida y se puede entregar.

Parámetros:

email (obligatorio): La dirección de correo electrónico para verificar
custom_data (opcional): datos personalizados para asociar con la solicitud

Ejemplo:

result = await verify_email_send("john.doe@example.com")

2. Búsqueda de correo electrónico ( `find_email` )

Busque una dirección de correo electrónico para una persona según su nombre y el dominio de su empresa.

Parámetros:

full_name (obligatorio): El nombre completo de la persona
domain (obligatorio): El dominio o sitio web de la empresa
linkedin_url (opcional): URL del perfil de LinkedIn para una mayor precisión
custom_data (opcional): datos personalizados para asociar con la solicitud

Ejemplo:

result = await find_email(
    full_name="John Doe",
    domain="example.com",
    linkedin_url="https://linkedin.com/in/johndoe"
)

3. Estado del trabajo ( `get_job_status` )

Comprobar el estado de un trabajo enviado anteriormente.

Parámetros:

job_id (obligatorio): El ID del trabajo a comprobar

Ejemplo:

result = await get_job_status("job_123456")

4. Lista de trabajos ( `list_jobs` )

Enumere todos los trabajos (Nota: este punto final puede tener disponibilidad limitada).

Ejemplo:

result = await list_jobs()

Formato de respuesta de la API

Verificación de correo electrónico exitosa

{
  "id": "job_123456",
  "status": "completed",
  "result": {
    "email": "john.doe@example.com",
    "valid": true,
    "deliverable": true,
    "confidence": 0.95,
    "verification_type": "smtp_catchall"
  }
}

Búsqueda exitosa de correos electrónicos

{
  "id": "job_789012",
  "status": "completed",
  "result": {
    "email": "john.doe@example.com",
    "confidence": 0.88,
    "sources": ["pattern_matching", "web_scraping"]
  }
}

Manejo de errores

El servidor gestiona varios escenarios de error:

Claves API no válidas
Limitación de velocidad
Tiempos de espera de la red
Formatos de correo electrónico no válidos
Errores de verificación de dominio

Respuestas de error comunes:

{
  "error": "Invalid API key",
  "code": 401
}

Configuración

Variables de entorno

TRYKITT_API_KEY : Su clave API de TryKitt.ai (obligatoria)

Configuración SSL

El servidor está configurado para funcionar con los puntos finales de la API de TryKitt.ai. La verificación SSL está deshabilitada por compatibilidad.

Desarrollo

Estructura del proyecto

trykittai-mcp-server/
├── server.py          # Main FastMCP server implementation
├── pyproject.toml     # Project dependencies and configuration
├── uv.lock           # Dependency lock file
├── README.md         # This file
├── LICENSE           # MIT License
└── .venv/            # Virtual environment

Dependencias

fastmcp : marco FastMCP para crear servidores MCP
httpx : Cliente HTTP asíncrono para solicitudes API
pydantic : Validación de datos y gestión de configuraciones

Acerca de TryKitt.ai

TryKitt.ai es un servicio avanzado de verificación y búsqueda de correo electrónico que:

Proporciona verificación de correo electrónico gratuita e ilimitada para usuarios individuales
Logra tasas de rebote <0,1% mediante verificación avanzada
Funciona de 2 a 5 veces más rápido que las soluciones alternativas
Utiliza servidores de identidad empresarial para la verificación general
Detecta cambios de trabajo y los valida contra sistemas reales

Obtenga más información en https://trykitt.ai/

Licencia

Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.

Contribuyendo

Bifurcar el repositorio
Crear una rama de características
Realiza tus cambios
Agregue pruebas si corresponde
Enviar una solicitud de extracción

Apoyo

Para cuestiones relacionadas con:

Este servidor MCP: Abra un problema en este repositorio
API de TryKitt.ai: Contacta con el soporte de TryKitt.ai
Marco FastMCP: consulte la documentación de FastMCP

Registro de cambios

versión 1.0.0

Versión inicial con verificación de correo electrónico y capacidades de búsqueda
Seguimiento del estado del trabajo
Soporte de procesamiento en tiempo real
Integración de FastMCP

Available Tools

4 tools

find_emailC

Find an email address for a person.

Args:
    full_name: The full name of the person
    domain: The company domain or website
    linkedin_url: Optional LinkedIn profile URL
    custom_data: Optional custom data to associate with the request

ParametersJSON Schema

Name	Required	Description	Default
`full_name`	Yes
`domain`	Yes
`linkedin_url`	No
`custom_data`	No

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'finds' an email address, implying a read-only operation, but does not specify accuracy, data sources, rate limits, or authentication needs. For a tool with no annotations and potential privacy implications, this is a significant gap in transparency.

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 concise and front-loaded with the purpose, followed by parameter details. It uses a clear structure with bullet points for args. However, the parameter explanations are very brief and could be more informative, slightly reducing efficiency.

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

Completeness2/5

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

Given the complexity of finding email addresses, no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on return values, error handling, data sources, and accuracy, which are crucial for effective tool use. The description does not adequately compensate for the missing structured data.

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

Parameters3/5

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

The description adds minimal semantics beyond the input schema. It lists parameters with brief explanations (e.g., 'full_name: The full name of the person'), but with 0% schema description coverage, it does not fully compensate. The explanations are basic and do not provide format details or usage examples, leaving gaps for the required parameters.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Find an email address for a person.' It specifies the verb ('find') and resource ('email address'), but does not distinguish it from sibling tools like 'verify_email_send', which might have overlapping functionality. The purpose is specific but lacks sibling differentiation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'verify_email_send' or specify contexts where this tool is preferred. Usage is implied only through the parameter descriptions, but no explicit when/when-not instructions are given.

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

get_job_statusB

Get the status of a job.

Args:
    job_id: The ID of the job to check

ParametersJSON Schema

Name	Required	Description	Default
`job_id`	Yes

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] the status of a job,' implying a read-only operation, but doesn't clarify aspects like whether it requires authentication, has rate limits, returns specific status formats (e.g., pending, completed), or handles errors. This leaves significant gaps for an agent to understand how to use it effectively.

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

Conciseness5/5

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

The description is highly concise and well-structured. It starts with a clear purpose statement, followed by a brief 'Args' section that lists the parameter with a simple explanation. There's no unnecessary information, and every sentence serves a functional role in guiding usage.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like authentication needs, error handling, or what the status output looks like (e.g., string values, timestamps). For a tool that likely returns critical operational data, this leaves the agent without enough context to use it reliably in complex scenarios.

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?

The description adds meaningful context for the single parameter: 'job_id: The ID of the job to check.' This clarifies that 'job_id' is an identifier used to retrieve status, which is helpful since schema description coverage is 0% (the schema only provides a title and type without explanation). With one parameter, the baseline is 4, and the description compensates well by explaining its purpose.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Get the status of a job.' It uses a specific verb ('Get') and resource ('status of a job'), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'list_jobs', which might provide a broader overview rather than specific status checks.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_jobs' for listing multiple jobs or other tools for related operations. There's no context about prerequisites, such as needing a job ID from another operation, or when this tool is appropriate.

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

list_jobsD

List jobs

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.8/5.0

Behavior1/5

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 but offers none. 'List jobs' doesn't indicate whether this is a read-only operation, whether it requires authentication, what format results are returned in, if there are rate limits, or any other behavioral characteristics. The description fails to provide any operational context beyond the basic action.

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

Conciseness2/5

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

While technically concise with just two words, this description represents under-specification rather than effective brevity. The single phrase 'List jobs' fails to provide necessary context that would help an agent understand when and how to use this tool. Conciseness should not come at the expense of clarity and completeness.

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

Completeness1/5

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

For a tool with no annotations and no output schema, the description 'List jobs' is completely inadequate. It doesn't explain what constitutes a 'job' in this context, what information is returned, whether results are paginated, or any other operational details. The description fails to provide the minimal context needed for effective tool invocation.

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?

The tool has zero parameters, and the input schema has 100% description coverage (though empty). With no parameters to document, the description doesn't need to compensate for schema gaps. The baseline for zero-parameter tools is 4, as there's no parameter semantics burden on the description.

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

Purpose2/5

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

The description 'List jobs' is a tautology that merely restates the tool name without adding meaningful context. It specifies the verb ('list') and resource ('jobs'), but provides no differentiation from sibling tools like 'get_job_status' or additional scope information. This minimal description fails to clarify what type of jobs are being listed or under what conditions.

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

Usage Guidelines1/5

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

The description provides absolutely no guidance on when to use this tool versus alternatives like 'get_job_status' or other sibling tools. There's no mention of appropriate contexts, prerequisites, or exclusions. Users must infer usage patterns from the tool name alone, which is insufficient for effective tool selection.

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

verify_email_sendC

verify an email using trykitt.

Args:
    email: The email address to verify
    custom_data: Optional custom data to associate with the request

ParametersJSON Schema

Name	Required	Description	Default
`email`	Yes
`custom_data`	No

TDQS

C2.6/5.0

Behavior2/5

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 mentions 'verify an email using trykitt' but fails to explain key traits like whether this is a read-only or mutative operation, what the expected outcome is (e.g., sends an email, returns a status), or any rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior.

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 brief and front-loaded with the main purpose, followed by parameter details in a structured 'Args:' section. It avoids unnecessary elaboration, but the lack of context and behavioral details means it could be more informative without sacrificing conciseness.

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

Completeness2/5

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

Given the tool's complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'verify' means in practice, what happens after invocation (e.g., sends an email, returns a job ID), or how it relates to sibling tools, leaving the agent with insufficient context for effective use.

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

Parameters3/5

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

The description lists parameters ('email' and 'custom_data') and notes that 'custom_data' is optional, adding basic semantics beyond the input schema. However, with 0% schema description coverage, it doesn't fully compensate by explaining parameter formats (e.g., email validation rules, custom_data structure), leaving the agent with incomplete information for proper usage.

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

Purpose3/5

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

The description states 'verify an email using trykitt' which provides a basic verb+resource combination, but it's vague about what verification entails (e.g., sending a verification email, checking validity). It doesn't distinguish from siblings like 'find_email' or 'get_job_status', leaving ambiguity about the specific action.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like 'find_email' or 'get_job_status'. The description lacks context about prerequisites, such as whether this initiates a verification process or checks an existing one, leaving the agent without clear usage instructions.

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.

4 tool updatesAug 15, 2025
- First observedfind_email
- First observedget_job_status
- First observedlist_jobs
- First observedverify_email_send

Deploy Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

C2.9/5.0

Scored 2026-04-18 10:00 across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: find_email locates email addresses, get_job_status checks job status, list_jobs enumerates jobs, and verify_email_send validates emails. The descriptions clearly differentiate their functions, eliminating any potential for agent misselection.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (find_email, get_job_status, list_jobs, verify_email_send), with all tools using snake_case. The minor deviation is 'verify_email_send' which includes an extra verb 'send', but overall the pattern is predictable and readable.

Tool Count5/5

With 4 tools, this server is well-scoped for its purpose of email and job management. Each tool earns its place by covering distinct aspects: email discovery, job tracking, and email verification, without being overly sparse or bloated.

Completeness3/5

The tool surface covers core operations like finding and verifying emails, and managing jobs, but has notable gaps. For example, there are no tools to create or delete jobs, or to handle email sending beyond verification, which could limit agent workflows in this domain.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Reddit Discussion

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Appeared in Searches

Search for 'tc34jqkh7w'

Servidor mcp de TryKitt.ai

Características

Instalación

Configuración

Uso

Ejecución del servidor

Agregar a clientes MCP

Escritorio de Claude

Otros clientes de MCP

Uso con uv

Herramientas disponibles

1. Verificación de correo electrónico ( verify_email_send )

2. Búsqueda de correo electrónico ( find_email )

3. Estado del trabajo ( get_job_status )

4. Lista de trabajos ( list_jobs )

Formato de respuesta de la API

Verificación de correo electrónico exitosa

Búsqueda exitosa de correos electrónicos

Manejo de errores

Configuración

Variables de entorno

Configuración SSL

Desarrollo

Estructura del proyecto

Dependencias

Acerca de TryKitt.ai

Licencia

Contribuyendo

Apoyo

Registro de cambios

versión 1.0.0

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches

1. Verificación de correo electrónico ( `verify_email_send` )

2. Búsqueda de correo electrónico ( `find_email` )

3. Estado del trabajo ( `get_job_status` )

4. Lista de trabajos ( `list_jobs` )