Implementación de servidor del Protocolo de Contexto de Modelo (MCP) para interactuar con la API de Kayzen Analytics. Este paquete permite que los modelos de IA accedan y analicen los datos de las campañas publicitarias de Kayzen mediante una interfaz estandarizada.
Autenticación automatizada : gestión de tokens integrada con mecanismo de actualización automática
Gestión de informes : Fácil acceso a los informes analíticos de Kayzen
Manejo de errores : manejo integral de errores para interacciones de API
Compatibilidad con TypeScript : implementación completa de TypeScript con definiciones de tipos
Configuración basada en el entorno : configuración sencilla mediante variables de entorno
Related MCP server: Amazon Ads API MCP SDK
npm install @feedmob-ai/kayzen-mcpCrea un archivo .env con tus credenciales de Kayzen:
KAYZEN_USERNAME=your_username
KAYZEN_PASSWORD=your_password
KAYZEN_BASIC_AUTH=your_basic_auth_token
KAYZEN_BASE_URL=https://api.kayzen.io/v1 # Optional, defaults to this valueimport { KayzenMCPServer } from '@feedmob-ai/kayzen-mcp';
const server = new KayzenMCPServer();
server.start();list_reportsEnumera todos los informes disponibles de Kayzen Analytics.
Entradas : Ninguna
Devuelve : Matriz de objetos de informe que contiene:
id : Identificador del informe
name : Nombre del informe
type : Tipo de informe
const reports = await server.tools.list_reports();get_report_resultsRecupera resultados para un informe específico.
Entradas :
report_id (cadena, obligatoria): ID del informe a obtener
start_date (cadena, opcional): Fecha de inicio en formato AAAA-MM-DD
end_date (cadena, opcional): Fecha de finalización en formato AAAA-MM-DD
Devuelve : Datos y metadatos del informe
const results = await server.tools.get_report_results({
report_id: 'report_id',
start_date: '2024-01-01', // optional
end_date: '2024-01-31' // optional
});analyze_report_results (Indicación)Analiza los resultados del informe y proporciona información.
Entradas :
report_id (cadena): ID del informe a analizar
El análisis incluye :
Métricas de rendimiento
Tendencias clave
Áreas de optimización
Patrones o anomalías inusuales
Para usar esto con Claude Desktop, agregue lo siguiente a su claude_desktop_config.json :
{
"mcpServers": {
"github": {
"command": "npx",
"args": [
"-y",
"@feedmob-ai/kayzen-mcp"
],
"env": {
"KAYZEN_USERNAME": "username",
"KAYZEN_PASSWORD": "pasword",
"KAYZEN_BASIC_AUTH": "auth token"
}
}
}
}Node.js (v16 o superior)
npm (v7 o superior)
Credenciales de la API de Kayzen
# Install dependencies
npm install
# Build the project
npm run build
# Start the server
npm start
# Development mode with hot-reload
npm run devkayzen-mcp/
├── src/
│ ├── server.ts # MCP server implementation
│ └── kayzen-client.ts # Kayzen API client
├── dist/ # Compiled JavaScript
└── package.json # Project configurationDependencias principales:
@modelcontextprotocol/sdk : ^1.7.0
axios : ^1.8.3
dotenv : ^16.4.7
zod : ^3.24.2
El servidor gestiona varios escenarios de error:
Errores de autenticación
Solicitudes de API no válidas
Problemas de red
Vencimiento y actualización del token
Parámetros no válidos
Licencia MIT
FeedMob
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | No | End date in YYYY-MM-DD format | |
| report_id | Yes | ID of the report to fetch results for | |
| start_date | No | Start date in YYYY-MM-DD format |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no 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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0get_report_resultslist_reportsScored across 2 tools
The two tools have clearly distinct purposes: 'get_report_results' appears to retrieve specific report data, while 'list_reports' enumerates available reports. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on whether it needs to list reports or fetch results from a particular report.
Both tools follow a consistent verb_noun naming pattern ('get_report_results' and 'list_reports'), using snake_case throughout. The verbs 'get' and 'list' are standard and predictable, making the tool names easy to understand and use without confusion.
With only two tools, the server feels under-scoped for an analytics domain, which typically involves operations like creating, updating, filtering, or deleting reports. This minimal set may force agents to work around gaps, such as being unable to generate new reports or modify existing ones, limiting functionality.
The tool surface is severely incomplete for analytics: it allows listing and retrieving reports but lacks essential operations like creating, updating, deleting, or filtering reports. This creates significant gaps that will likely cause agent failures when trying to perform common analytics tasks beyond basic read-only access.
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