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
EasyAiFlows MCP 服务器
一个 MCP (Model Context Protocol) 服务器，旨在帮助 AI 助手评估企业的自动化就绪程度，并提供特定行业的 AI 自动化建议。
当用户询问 Claude、ChatGPT 或任何兼容 MCP 的 AI 助手“我该如何自动化我的业务？”时，此服务器将提供包含真实自动化示例和后续步骤的个性化评估。
工具
assess_business_automation
根据企业的行业和痛点评估其 AI 自动化就绪程度。
参数：
参数 | 必需 | 描述 |
| 是 | 业务行业（例如：“牙医”、“餐厅”、“暖通空调”） |
| 否 | 具体痛点数组（例如：["漏接电话", "爽约"]） |
| 否 | 团队规模：“个人”、“2-5人”、“6-15人”、“16-50人”、“50人以上” |
| 否 | 当前使用的工具数组（例如：["Google Sheets", "Mailchimp"]） |
返回： 自动化就绪评分（0-100）、特定行业的痛点、节省时间的推荐自动化方案，以及包含预约链接的后续步骤。
get_automation_examples
获取特定行业 AI 自动化的真实案例。
参数：
参数 | 必需 | 描述 |
| 是 | 获取案例的业务行业 |
返回： 3 个经过验证的自动化方案，包含描述、每周节省的时间、整体影响统计数据以及完整行业指南的链接。
Related MCP server: essetech-ai-readiness-mcp
支持的行业 (20)
牙医、餐厅、暖通空调 (HVAC)、房地产、健身房、理发店、美甲沙龙、医疗水疗中心、脊椎按摩师、保险代理人、抵押贷款经纪人、摄影师、活动策划、清洁服务、园艺景观、汽车维修、宠物美容、托儿所、教会、非营利组织
该服务器还处理别名（例如：“健身房” → 健身房， “修理工” → 汽车维修），并为未列出的行业提供通用评估。
安装
Claude Desktop
添加到您的 Claude Desktop 配置文件中（Mac 上为 ~/Library/Application Support/Claude/claude_desktop_config.json，Windows 上为 %APPDATA%Claudeclaude_desktop_config.json）：
{
"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从源码构建
git clone https://github.com/Ronnie-Nutrition/easyaiflows-mcp-server.git
cd easyaiflows-mcp-server
npm install
npm run build使用示例
安装完成后，您可以向 AI 助手询问如下内容：
“评估我餐厅的自动化就绪程度——我们是一个 5 人团队，经常漏接电话，而且评论无人回复。”
“牙科诊所有哪些 AI 自动化方案？”
“我经营一家个人清洁服务公司，所有事情都用 Google Sheets 处理。AI 能提供什么帮助？”
“向我展示保险代理人的自动化示例。”
关于 EasyAiFlows
为准备好停止苦干并开始增长的企业家提供定制化 AI 自动化服务。由德克萨斯州皮尔兰的 Ronnie Craig 构建。
AI 就绪程度评估器：https://easyaiflows.com/grader
许可证
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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