A
licenseNot graded
qualityB
maintenanceEnables AI agents to check whether AI assistants recommend a brand and audit a site's AI-agent readiness, providing visibility scores and specific gaps.
MIT
EasyAiFlows Automation Assessment
Servidor MCP de EasyAiFlows
Un servidor MCP (Model Context Protocol) que ayuda a los asistentes de IA a evaluar la preparación de una empresa para la automatización y a proporcionar recomendaciones de automatización con IA específicas para cada sector.
Cuando un usuario le pregunta a Claude, ChatGPT o cualquier asistente de IA compatible con MCP "¿cómo automatizo mi negocio?", este servidor proporciona una evaluación personalizada con ejemplos reales de automatización y los siguientes pasos a seguir.
Herramientas
assess_business_automation
Evalúa la preparación de una empresa para la automatización con IA en función de su sector y sus puntos débiles.
Parámetros:
Parámetro | Requerido | Descripción |
| Sí | Sector empresarial (p. ej., "dentistas", "restaurantes", "climatización") |
| No | Matriz de puntos débiles específicos (p. ej., ["llamadas perdidas", "ausencias"]) |
| No | Tamaño del equipo: "solo", "2-5", "6-15", "16-50", "50+" |
| No | Matriz de herramientas utilizadas actualmente (p. ej., ["Google Sheets", "Mailchimp"]) |
Devuelve: Puntuación de preparación para la automatización (0-100), puntos débiles específicos del sector, automatizaciones recomendadas con el tiempo ahorrado y los siguientes pasos con un enlace de reserva.
get_automation_examples
Obtenga ejemplos reales de automatizaciones con IA para un sector específico.
Parámetros:
Parámetro | Requerido | Descripción |
| Sí | Sector empresarial para el que obtener ejemplos |
Devuelve: 3 automatizaciones probadas con descripciones, tiempo ahorrado por semana, estadísticas de impacto general y enlaces a la guía completa del sector.
Related MCP server: essetech-ai-readiness-mcp
Sectores admitidos (20)
Dentistas, Restaurantes, Climatización (HVAC), Inmobiliarias, Gimnasios, Barberías, Salones de manicura, Spas médicos, Quiroprácticos, Agentes de seguros, Corredores hipotecarios, Fotógrafos, Organizadores de eventos, Servicios de limpieza, Paisajistas, Reparación de automóviles, Peluquerías caninas, Guarderías, Iglesias, Organizaciones sin ánimo de lucro
El servidor también gestiona alias (p. ej., "gimnasio" → fitness-studios, "mecánico" → auto-repair) y proporciona una evaluación genérica para sectores no listados.
Instalación
Claude Desktop
Añada a su configuración de Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json en Mac o %APPDATA%\Claude\claude_desktop_config.json en Windows):
{
"mcpServers": {
"easyaiflows": {
"command": "node",
"args": ["/path/to/easyaiflows-mcp-server/dist/server.js"]
}
}
}Claude Code
claude mcp add easyaiflows node /path/to/easyaiflows-mcp-server/dist/server.jsCompilar desde el código fuente
git clone https://github.com/Ronnie-Nutrition/easyaiflows-mcp-server.git
cd easyaiflows-mcp-server
npm install
npm run buildEjemplo de uso
Una vez instalado, pregunte a su asistente de IA cosas como:
"Evalúa la preparación de mi restaurante para la automatización: somos un equipo de 5, perdemos muchas llamadas telefónicas y nuestras reseñas se quedan sin respuesta."
"¿Qué automatizaciones con IA existen para las consultas dentales?"
"Dirijo un servicio de limpieza yo solo y uso Google Sheets para todo. ¿Cómo puede ayudarme la IA?"
"Muéstrame ejemplos de automatización para agentes de seguros."
Acerca de EasyAiFlows
Automatización personalizada con IA para emprendedores que están listos para dejar de trabajar duro y empezar a crecer. Creado por Ronnie Craig en Pearland, TX.
Sitio web: https://easyaiflows.com
Evaluador de preparación para IA: https://easyaiflows.com/grader
Reserve una llamada de estrategia gratuita: https://tidycal.com/ronnieysela/ai-strategy-call
Licencia
MIT
Available Tools
2 toolsassess_business_automationA
Assess a business's AI automation readiness based on their industry and pain points. Returns a personalized automation score, specific recommendations, and next steps.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| industry | Yes | The business industry (e.g., 'dentists', 'restaurants', 'hvac', 'real-estate', 'fitness-studios', 'barbershops', 'nail-salons', 'med-spas', 'chiropractors', 'insurance-agents', 'mortgage-brokers', 'photographers', 'event-planners', 'cleaning-services', 'landscapers', 'auto-repair', 'pet-groomers', 'daycares', 'churches', 'nonprofits') | |
| pain_points | No | Specific pain points the business is experiencing (e.g., 'missed calls', 'no-shows', 'slow lead response') | |
| team_size | No | Number of people on the team | |
| current_tools | No | Tools currently being used (e.g., 'Google Sheets', 'QuickBooks', 'Mailchimp') |
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It indicates the tool returns data (score, recommendations, next steps) but does not explicitly state whether it is read-only or if there are any side effects. This is adequate but could be more transparent.
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 concise with two sentences, front-loading the purpose and output. Every word adds value—no fluff, no redundancy. It efficiently communicates the tool's core function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 4 parameters (with only one required), no output schema, and no annotations, the description adequately explains the tool's purpose and output. It could note that most parameters are optional, but the schema's 'required' field covers that. Overall, sufficient for an AI agent to understand usage.
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?
Schema description coverage is 100%, so baseline is 3. The description does not add any parameter-specific information beyond what is already in the schema; it only mentions 'industry and pain points' which are already documented. No additional semantic value is provided.
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: 'Assess a business's AI automation readiness' based on industry and pain points, and details the output: 'a personalized automation score, specific recommendations, and next steps.' This distinguishes it from the sibling tool 'get_automation_examples' which likely provides examples rather than an assessment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use: when needing an automation readiness assessment. However, it does not explicitly contrast with the sibling tool 'get_automation_examples' or provide when-not-to-use scenarios. The context is clear but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_automation_examplesA
Get real examples of AI automations for a specific industry, including what they do, time saved, and revenue impact.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| industry | Yes | The business industry to get examples for (e.g., 'dentists', 'restaurants', 'real-estate') |
TDQS
A3.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully disclose behavior. It only mentions returned content types but omits details like read-only nature, authorization needs, rate limits, or response format.
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?
Single, front-loaded sentence that efficiently communicates purpose and output without superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers key output aspects but could include example count or response structure for greater completeness.
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?
Schema coverage is 100% with a clear parameter description; the tool description adds no additional meaning beyond what the schema already provides, achieving baseline.
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?
Description uses specific verb 'Get' and resource 'real examples of AI automations', clearly differentiating from sibling 'assess_business_automation' which assesses rather than retrieves examples.
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?
No explicit when-to-use or when-not-to-use guidance; context hints at usage for specific industries but does not differentiate from sibling tool or provide exclusion criteria.
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.
2 tool updates
v1.0.0- First observed
assess_business_automation - First observed
get_automation_examples
TDQS
A3.7/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: one assesses automation readiness and provides recommendations, the other gives industry-specific examples. No overlap or ambiguity.
Naming Consistency5/5
Both tool names follow a consistent verb_noun pattern (assess_* and get_*), making them predictable and easy to understand.
Tool Count3/5
With only 2 tools, the server feels thin for a comprehensive automation assessment service. While it covers core tasks, the count is borderline low for its apparent scope.
Completeness2/5
The tool surface is minimal, lacking capabilities to manage assessments over time, compare results, or handle follow-up actions. Significant gaps exist beyond one-shot queries.
Maintenance
ActivityInactive
ResponsivenessNo issues
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