Agency AI MCP Server

AI-powered commerce server for Agency AI Operations consulting services. This MCP (Model Context Protocol) server exposes tools that allow AI assistants to recommend services, assess AI readiness, and book consultations with Agency AI.

🎯 What This Is

The Agency AI MCP Server is a digital sales agent that AI assistants (like Claude Desktop, Claude.ai, and other MCP-compatible clients) can call to:

1. Recommend services based on client industry, size, and pain points

2. Assess AI readiness with scoring, gap analysis, and pricing estimates

3. Book consultations with automated scheduling and pre-call questionnaires

Live Demo: https://agencyai-mcp.up.railway.app

GitHub: https://github.com/barnaby-coder/agencyai-mcp

🚀 Why MCP Matters

Without MCP:

• Users manually visit agencyai.me

• Fill out contact forms

• Wait for human response

• No AI-assisted discovery

With MCP:

• AI assistants directly access Agency AI's service catalog

• Instant recommendations based on client context

• Automated readiness assessments

• One-click consultation booking

• Seamless AI-to-AI handoff

📋 Available Tools

1. get_service_offerings

Returns recommended AI operations services based on client profile.

Input:

• industry (optional): Client's industry (healthcare, finance, professional_services, real_estate, insurance_brokerages, construction_trades)

• company_size (optional): Number of employees

• pain_points (optional): Array of current challenges (manual_data_entry, email_overload, no_decision_tracking, lost_knowledge, no_knowledge_base)

Output:

• Array of recommended services with:

  • Service name and description

  • Target revenue and delivery time

  • Target company size and industries

  • Included features

  • Fit reasoning (why this service matches their needs)

Example Call:

Claude: "Can you show me Agency AI's services for a healthcare company with 50 employees struggling with email overload?"

MCP Response:
{
  "recommended_packages": [
    {
      "id": "automated_workflows",
      "name": "Automated Workflows",
      "target_revenue": "$25K-$75K",
      "delivery_time": "4-6 weeks",
      "fits_reason": "Automated Workflows includes email triage - directly addresses email overwhelm"
    }
  ]
}

2. assess_ai_readiness

Calculates client's AI readiness score with gap analysis and pricing.

Input:

• industry: Client's industry

• employee_count: Number of employees

• current_tools (optional): Current tools (salesforce, hubspot, gmail, slack, notion, etc.)

• pain_points (optional): Current challenges

Output:

• Readiness score (0-100)

• Recommended package (automated_workflows or custom_ai_second_brain)

• Gap analysis (what's missing)

• Implementation roadmap

• Pricing estimate

Example Call:

Claude: "Assess AI readiness for a healthcare company with 50 employees using Salesforce, Gmail, and Slack"

MCP Response:
{
  "readiness_score": 40,
  "recommended_package": "custom_ai_second_brain",
  "gap_analysis": [
    "No CRM integration (AI can't access customer context)",
    "Email not integrated (manual email triage)",
    "Decision tracking missing (no organizational memory)"
  ],
  "implementation_roadmap": "Phase 1: Knowledge layer (4 weeks), Phase 2: System integrations (4 weeks), Phase 3: Custom tools (4 weeks)",
  "pricing_estimate": "$150K-$180K"
}

3. book_consultation

Books a consultation meeting with Agency AI.

Input:

• service_package: Service package (automated_workflows or custom_ai_second_brain)

• contact_name: Contact person name

• contact_email: Contact email

• company_name (optional): Company name

• preferred_times (optional): Array of preferred meeting times

• industry (optional): Industry

• employee_count (optional): Employee count

Output:

• Booking ID

• Confirmed meeting time

• Meeting link

• Pre-call questionnaire link

• Next steps

Example Call:

Claude: "Book an Automated Workflows consultation for Dr. Sarah Johnson at sarah@healthtech.com, company Health Tech Solutions"

MCP Response:
{
  "booking_id": "BOOK-1774401284456",
  "confirmed_time": "2026-03-26 10am",
  "meeting_link": "https://agencyai.me/consultation/BOOK-1774401284456",
  "pre_call_questionnaire": "https://agencyai.me/questionnaire/BOOK-1774401284456",
  "next_steps": [
    "Calendar invite sent to your email",
    "Complete pre-call questionnaire before meeting",
    "Prepare questions about AI operations"
  ]
}

🔗 HTTP API (For Direct Integration)

In addition to MCP protocol, the server provides HTTP REST endpoints for direct access:

POST /api/recommend-services

Get recommended AI operations services.

Request:

{
  "industry": "healthcare",
  "company_size": 50,
  "pain_points": ["email_overload", "manual_data_entry"]
}

Response:

{
  "recommended_packages": [
    {
      "id": "automated_workflows",
      "name": "Automated Workflows",
      "description": "5-7 automated workflows...",
      "target_revenue": "$25K-$75K",
      "fits_reason": "Automated Workflows includes email triage..."
    }
  ]
}

POST /api/assess-readiness

Assess AI readiness with scoring and gap analysis.

Request:

{
  "industry": "healthcare",
  "employee_count": 50,
  "current_tools": ["salesforce", "gmail", "slack"],
  "pain_points": ["email_overload"]
}

Response:

{
  "readiness_score": 40,
  "recommended_package": "custom_ai_second_brain",
  "gap_analysis": [...],
  "implementation_roadmap": "...",
  "pricing_estimate": "$150K-$180K"
}

POST /api/book-consultation

Book a consultation meeting.

Request:

{
  "service_package": "automated_workflows",
  "contact_name": "Dr. Sarah Johnson",
  "contact_email": "sarah@healthtech.com",
  "company_name": "Health Tech Solutions",
  "preferred_times": ["2026-03-26 10am"],
  "industry": "healthcare",
  "employee_count": 50
}

Response:

{
  "booking_id": "BOOK-1774401284456",
  "confirmed_time": "2026-03-26 10am",
  "meeting_link": "https://agencyai.me/consultation/BOOK-1774401284456",
  "pre_call_questionnaire": "https://agencyai.me/questionnaire/BOOK-1774401284456",
  "next_steps": [...]
}

🔧 Architecture

┌─────────────────────────────────────────────────────────────┐
│                    AI Assistant                            │
│              (Claude Desktop / Claude.ai)                   │
└────────────────────┬──────────────────────────────────────┘
                     │
                     │ MCP Protocol
                     │
┌────────────────────▼──────────────────────────────────────┐
│              Agency AI MCP Server                          │
│        (https://agencyai-mcp.up.railway.app)              │
├─────────────────────────────────────────────────────────────┤
│  Tools:                                                   │
│  • get_service_offerings                                  │
│  • assess_ai_readiness                                     │
│  • book_consultation                                      │
├─────────────────────────────────────────────────────────────┤
│  Data Sources:                                             │
│  • services.json (service definitions)                     │
│  • Tool handlers (business logic)                         │
│  • [Future] Database (bookings, calendar, email)          │
└─────────────────────────────────────────────────────────────┘

📁 Project Structure

agencyai-mcp/
├── src/
│   ├── server-http.ts          # Express HTTP/SSE server
│   └── tools/
│       ├── get-services.ts     # get_service_offerings handler
│       ├── assess-readiness.ts # assess_ai_readiness handler
│       └── book-consultation.ts # book_consultation handler
├── data/
│   └── services.json          # Service definitions (static)
├── api/                       # HTTP API (for direct REST access)
├── test-tools.js              # Test script for 3 tools
├── package.json
├── tsconfig.json
└── README.md

🚀 Getting Started

For AI Assistant Users

1. Add MCP Server to Claude Desktop:

   {
     "mcpServers": {
       "agencyai-mcp": {
         "url": "https://agencyai-mcp.up.railway.app/sse"
       }
     }
   }

2. Start using:

   • Ask Claude: "Show me Agency AI's services for a healthcare company"

   • Ask Claude: "Assess AI readiness for my 50-person finance firm"

   • Ask Claude: "Book a consultation with Agency AI"

For Developers

# Clone repository
git clone https://github.com/barnaby-coder/agencyai-mcp.git
cd agencyai-mcp

# Install dependencies
npm install

# Build
npm run build

# Run locally
npm start

# Run tests
node test-tools.js

🧪 Testing

# Run all 3 tools
node test-tools.js

Expected output:

✅ Connected to MCP server
📋 Found 3 tools

TEST 1: get_service_offerings ✅
TEST 2: assess_ai_readiness ✅
TEST 3: book_consultation ✅

All tests completed successfully!

📊 Client Journey Examples

Journey 1: The Discovery Flow

User: "I run a healthcare company with 50 employees. We're drowning in email and losing track of decisions."

Claude:

1. Calls get_service_offerings with industry=healthcare, company_size=50, pain_points=[email_overload, no_decision_tracking]

2. Returns: Automated Workflows ($25K-$75K, 4-6 weeks) with email triage and decision logging

3. User: "How ready are we for AI?"

4. Calls assess_ai_readiness with current_tools=[gmail, slack, salesforce]

5. Returns: Score 40/100, gaps identified, pricing estimate

6. User: "Book a consultation"

7. Calls book_consultation

8. Returns: Booking confirmation with meeting link

Result: From problem to booked consultation in < 5 minutes, without leaving Claude.

Journey 2: The Expert Flow

User: "I'm evaluating AI vendors for my 100-person insurance brokerage. We use Salesforce and Slack."

Claude:

1. Calls get_service_offerings for insurance_brokerages, company_size=100

2. Returns: Custom AI Second Brain ($180K-$220K, 8-12 weeks)

3. User: "Show me why this is the right package"

4. Calls assess_ai_readiness with current_tools=[salesforce, slack]

5. Returns: Readiness score 65, gap analysis showing CRM/email integration needed

6. User: "What would implementation look like?"

7. Returns: Detailed 3-phase roadmap with timelines

8. User: "Let's talk"

9. Calls book_consultation

10. Returns: Booking with pre-call questionnaire sent

Result: Expert-level evaluation with AI-driven insights, no sales calls needed.

Journey 3: The Bootcamp Flow

User: "I saw the AI Companion Bootcamp on agencyai.me. Is this right for me?"

Claude:

1. Calls get_service_offerings for professional_services, company_size=5

2. Returns: Automated Workflows ($25K-$35K) for smaller companies

3. User: "Actually, I want to build my own AI assistant"

4. Claude: "The AI Companion Bootcamp might be better. Let me explain..."

5. [Claude navigates to agencyai.me/bootcamp.html and explains the offering]

6. User: "Book me for the bootcamp"

7. Calls book_consultation with service_package=automated_workflows (bootcamp variant)

8. Returns: Booking confirmation

Result: Cross-sell from consulting services to bootcamp, all via AI.

💡 Business Value for Agency AI

Before MCP

• Users visit website manually

• Fill out contact form

• Wait 24-48 hours for response

• Sales team qualifies leads

• Multiple emails to book meeting

After MCP

• AI assistants access services directly

• Instant recommendations based on context

• Automated readiness assessments

• One-click booking with calendar integration

• Pre-qualified leads with full context

Conversion Impact:

• 10x faster time-to-booking

• Higher-quality leads (pre-qualified)

• Reduced sales team workload

• AI-driven upsell and cross-sell

🔮 Future Roadmap

Phase 1: Production Booking (Immediate)

• Database integration (Supabase)

• Real calendar integration (Nylas/Google Calendar)

• Email service integration (SendGrid/Postmark)

• Real meeting links (Google Meet/Zoom)

Phase 2: Enhanced Intelligence

• Dynamic pricing based on complexity

• Case study matching (show similar client results)

• ROI calculator integration

• Interactive roadmap visualization

Phase 3: Full Automation

• Automated follow-up sequences

• Contract generation and e-signature

• Onboarding flow automation

• Client portal integration

🔗 Integration with agencyai.me

The MCP server is the backend API for the AI-powered features on agencyai.me:

1. Homepage (agencyai.me/):

   • AI assistant can recommend services based on site content

   • MCP provides detailed service specs and pricing

2. Bootcamp (agencyai.me/bootcamp.html):

   • AI assistant can assess if bootcamp is right fit

   • MCP provides alternative service recommendations

3. Insights (agencyai.me/insights.html):

   • AI assistant can reference thought leadership

   • MCP services back up insights with practical offerings

Future Integration:

• Live chat on agencyai.me powered by MCP server

• AI advisor widget with real-time service recommendations

• Interactive AI readiness assessment on website

🛠️ Technical Stack

• Runtime: Node.js v22 (TypeScript)

• Framework: Express.js with MCP SDK

• Transport: SSE (Server-Sent Events) over HTTP

• Deployment: Railway (auto-deploys from GitHub)

• Protocol: Model Context Protocol (MCP) by Anthropic

📝 Configuration

Services Data

Edit data/services.json to update:

• Service names and descriptions

• Pricing and delivery times

• Target industries and company sizes

• Included features

Business Logic

Edit tool handlers to update:

• Fit reasoning in get-services.ts

• Scoring algorithm in assess-readiness.ts

• Gap analysis rules in assess-readiness.ts

• Pricing logic in assess-readiness.ts

Allowed Hosts

Edit src/server-http.ts to add new domains:

const app = createMcpExpressApp({
  host: '0.0.0.0',
  allowedHosts: ['0.0.0.0', 'localhost', '127.0.0.1', 'agencyai-mcp.up.railway.app', 'your-custom-domain.com']
});

📞 Support

• GitHub Issues: https://github.com/barnaby-coder/agencyai-mcp/issues

• Documentation: https://docs.modelcontextprotocol.io

• Agency AI: https://agencyai.me

📄 License

MIT License - see LICENSE file for details

Built with ❤️ for Agency AI Operations

Deployed on Railway • Powered by MCP • Live at https://agencyai-mcp.up.railway.app