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 Server
An MCP (Model Context Protocol) server that helps AI assistants assess business automation readiness and provide industry-specific AI automation recommendations.
When a user asks Claude, ChatGPT, or any MCP-compatible AI assistant "how do I automate my business?" — this server provides a personalized assessment with real automation examples and next steps.
Tools
assess_business_automation
Assess a business's AI automation readiness based on their industry and pain points.
Parameters:
Parameter | Required | Description |
| Yes | Business industry (e.g., "dentists", "restaurants", "hvac") |
| No | Array of specific pain points (e.g., ["missed calls", "no-shows"]) |
| No | Team size: "solo", "2-5", "6-15", "16-50", "50+" |
| No | Array of tools currently used (e.g., ["Google Sheets", "Mailchimp"]) |
Returns: Automation readiness score (0-100), industry-specific pain points, recommended automations with time saved, and next steps with booking link.
get_automation_examples
Get real examples of AI automations for a specific industry.
Parameters:
Parameter | Required | Description |
| Yes | Business industry to get examples for |
Returns: 3 proven automations with descriptions, time saved per week, overall impact stats, and links to the full industry guide.
Related MCP server: essetech-ai-readiness-mcp
Supported Industries (20)
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
The server also handles aliases (e.g., "gym" → fitness-studios, "mechanic" → auto-repair) and provides a generic assessment for unlisted industries.
Installation
Claude Desktop
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on Mac or %APPDATA%\Claude\claude_desktop_config.json on 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.jsBuild from Source
git clone https://github.com/Ronnie-Nutrition/easyaiflows-mcp-server.git
cd easyaiflows-mcp-server
npm install
npm run buildExample Usage
Once installed, ask your AI assistant things like:
"Assess my restaurant's automation readiness — we're a team of 5, we miss a lot of phone calls, and our reviews are going unanswered."
"What AI automations exist for dental practices?"
"I run a cleaning service solo and use Google Sheets for everything. How can AI help?"
"Show me automation examples for insurance agents."
About EasyAiFlows
Custom AI automation for entrepreneurs who are ready to stop grinding and start growing. Built by Ronnie Craig in Pearland, TX.
Website: https://easyaiflows.com
AI Readiness Grader: https://easyaiflows.com/grader
Book a Free Strategy Call: https://tidycal.com/ronnieysela/ai-strategy-call
License
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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