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Ronnie-Nutrition

EasyAiFlows Automation Assessment

by Ronnie-Nutrition
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TypeScript

EasyAiFlows MCP 서버

AI 어시스턴트가 비즈니스 자동화 준비 상태를 평가하고 산업별 AI 자동화 권장 사항을 제공하도록 돕는 MCP(Model Context Protocol) 서버입니다.

사용자가 Claude, ChatGPT 또는 MCP 호환 AI 어시스턴트에게 "내 비즈니스를 어떻게 자동화할 수 있을까?"라고 물으면, 이 서버는 실제 자동화 사례와 다음 단계가 포함된 개인화된 평가를 제공합니다.

도구

assess_business_automation

산업 및 문제점을 기반으로 비즈니스의 AI 자동화 준비 상태를 평가합니다.

매개변수:

매개변수

필수

설명

industry

예

비즈니스 산업 (예: "치과", "식당", "HVAC")

pain_points

아니요

구체적인 문제점 배열 (예: ["부재중 전화", "노쇼"])

team_size

아니요

팀 규모: "1인", "2-5인", "6-15인", "16-50인", "50인 이상"

current_tools

아니요

현재 사용 중인 도구 배열 (예: ["Google Sheets", "Mailchimp"])

반환값: 자동화 준비 점수(0-100), 산업별 문제점, 시간 절약 효과가 포함된 권장 자동화, 예약 링크가 포함된 다음 단계.

get_automation_examples

특정 산업에 대한 실제 AI 자동화 사례를 가져옵니다.

매개변수:

매개변수

필수

설명

industry

예

사례를 가져올 비즈니스 산업

반환값: 설명, 주당 절약 시간, 전반적인 영향 통계 및 전체 산업 가이드 링크가 포함된 3가지 검증된 자동화 사례.

Related MCP server: essetech-ai-readiness-mcp

지원 산업 (20개)

치과, 식당, HVAC, 부동산, 피트니스 스튜디오, 이발소, 네일 살롱, 메디컬 스파, 카이로프랙틱, 보험 대리점, 모기지 브로커, 사진작가, 이벤트 플래너, 청소 서비스, 조경업체, 자동차 수리, 애완동물 미용, 어린이집, 교회, 비영리 단체

또한 이 서버는 별칭(예: "헬스장" → 피트니스 스튜디오, "정비소" → 자동차 수리)을 처리하며, 목록에 없는 산업에 대해서는 일반적인 평가를 제공합니다.

설치

Claude Desktop

Claude Desktop 설정 파일(~/Library/Application Support/Claude/claude_desktop_config.json(Mac) 또는 %APPDATA%\Claude\claude_desktop_config.json(Windows))에 추가하세요:

{
  "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.js

소스에서 빌드

git clone https://github.com/Ronnie-Nutrition/easyaiflows-mcp-server.git
cd easyaiflows-mcp-server
npm install
npm run build

사용 예시

설치 후 AI 어시스턴트에게 다음과 같이 질문해 보세요:

  • "우리 식당의 자동화 준비 상태를 평가해 줘. 5인 팀이고, 부재중 전화가 많으며, 리뷰에 답변을 못 하고 있어."

  • "치과를 위한 AI 자동화에는 어떤 것이 있을까?"

  • "나는 1인 청소 서비스를 운영하고 있고 모든 업무에 Google Sheets를 사용해. AI가 어떻게 도울 수 있을까?"

  • "보험 대리점을 위한 자동화 사례를 보여줘."

EasyAiFlows 소개

더 이상 고생하지 않고 성장을 시작하려는 기업가를 위한 맞춤형 AI 자동화 서비스입니다. 텍사스주 피어랜드의 Ronnie Craig가 구축했습니다.

라이선스

MIT

Available Tools

2 tools
assess_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
NameRequiredDescriptionDefault
industryYesThe 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_pointsNoSpecific pain points the business is experiencing (e.g., 'missed calls', 'no-shows', 'slow lead response')
team_sizeNoNumber of people on the team
current_toolsNoTools 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
NameRequiredDescriptionDefault
industryYesThe 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.

  1. 2 tool updatesv1.0.0
    • First observedassess_business_automation
    • First observedget_automation_examples
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

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

Resources

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