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Ronnie-Nutrition

EasyAiFlows Automation Assessment

by Ronnie-Nutrition
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

EasyAiFlows MCP-Server

Ein MCP-Server (Model Context Protocol), der KI-Assistenten dabei unterstützt, die Automatisierungsbereitschaft von Unternehmen zu bewerten und branchenspezifische KI-Automatisierungsempfehlungen zu geben.

Wenn ein Benutzer Claude, ChatGPT oder einen anderen MCP-kompatiblen KI-Assistenten fragt: „Wie automatisiere ich mein Unternehmen?“ – liefert dieser Server eine personalisierte Bewertung mit konkreten Automatisierungsbeispielen und nächsten Schritten.

Tools

assess_business_automation

Bewertet die KI-Automatisierungsbereitschaft eines Unternehmens basierend auf Branche und Schwachstellen.

Parameter:

Parameter

Erforderlich

Beschreibung

industry

Ja

Branche des Unternehmens (z. B. „Zahnärzte“, „Restaurants“, „HLK“)

pain_points

Nein

Liste spezifischer Schwachstellen (z. B. ["verpasste Anrufe", "Nichterscheinen"])

team_size

Nein

Teamgröße: "solo", "2-5", "6-15", "16-50", "50+"

current_tools

Nein

Liste der aktuell verwendeten Tools (z. B. ["Google Sheets", "Mailchimp"])

Rückgabe: Automatisierungsbereitschafts-Score (0-100), branchenspezifische Schwachstellen, empfohlene Automatisierungen mit Zeitersparnis sowie nächste Schritte mit Buchungslink.

get_automation_examples

Liefert konkrete Beispiele für KI-Automatisierungen für eine bestimmte Branche.

Parameter:

Parameter

Erforderlich

Beschreibung

industry

Ja

Branche, für die Beispiele abgerufen werden sollen

Rückgabe: 3 bewährte Automatisierungen mit Beschreibungen, wöchentlicher Zeitersparnis, allgemeinen Wirkungsstatistiken und Links zum vollständigen Branchenleitfaden.

Related MCP server: essetech-ai-readiness-mcp

Unterstützte Branchen (20)

Zahnärzte, Restaurants, HLK (Heizung/Lüftung/Klima), Immobilien, Fitnessstudios, Friseursalons, Nagelstudios, Med-Spas, Chiropraktiker, Versicherungsvertreter, Hypothekenmakler, Fotografen, Veranstaltungsplaner, Reinigungsdienste, Landschaftsgärtner, Autowerkstätten, Tierpfleger, Kindertagesstätten, Kirchen, gemeinnützige Organisationen

Der Server verarbeitet auch Aliase (z. B. „Fitnessstudio“ → fitness-studios, „Mechaniker“ → auto-repair) und bietet eine allgemeine Bewertung für nicht aufgeführte Branchen.

Installation

Claude Desktop

Fügen Sie dies zu Ihrer Claude Desktop-Konfiguration hinzu (~/Library/Application Support/Claude/claude_desktop_config.json unter Mac oder %APPDATA%\Claude\claude_desktop_config.json unter 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.js

Aus Quellcode erstellen

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

Anwendungsbeispiele

Fragen Sie nach der Installation Ihren KI-Assistenten beispielsweise:

  • „Bewerte die Automatisierungsbereitschaft meines Restaurants – wir sind ein 5-köpfiges Team, verpassen viele Anrufe und unsere Bewertungen bleiben unbeantwortet.“

  • „Welche KI-Automatisierungen gibt es für Zahnarztpraxen?“

  • „Ich betreibe einen Reinigungsdienst als Solo-Unternehmer und nutze für alles Google Sheets. Wie kann KI helfen?“

  • „Zeige mir Automatisierungsbeispiele für Versicherungsvertreter.“

Über EasyAiFlows

Individuelle KI-Automatisierung für Unternehmer, die bereit sind, das Hamsterrad zu verlassen und zu wachsen. Entwickelt von Ronnie Craig in Pearland, TX.

Lizenz

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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