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:

Recommend services based on client industry, size, and pain points
Assess AI readiness with scoring, gap analysis, and pricing estimates
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

Add MCP Server to Claude Desktop:

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

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:

Calls get_service_offerings with industry=healthcare, company_size=50, pain_points=[email_overload, no_decision_tracking]
Returns: Automated Workflows ($25K-$75K, 4-6 weeks) with email triage and decision logging
User: "How ready are we for AI?"
Calls assess_ai_readiness with current_tools=[gmail, slack, salesforce]
Returns: Score 40/100, gaps identified, pricing estimate
User: "Book a consultation"
Calls book_consultation
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:

Calls get_service_offerings for insurance_brokerages, company_size=100
Returns: Custom AI Second Brain ($180K-$220K, 8-12 weeks)
User: "Show me why this is the right package"
Calls assess_ai_readiness with current_tools=[salesforce, slack]
Returns: Readiness score 65, gap analysis showing CRM/email integration needed
User: "What would implementation look like?"
Returns: Detailed 3-phase roadmap with timelines
User: "Let's talk"
Calls book_consultation
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:

Calls get_service_offerings for professional_services, company_size=5
Returns: Automated Workflows ($25K-$35K) for smaller companies
User: "Actually, I want to build my own AI assistant"
Claude: "The AI Companion Bootcamp might be better. Let me explain..."
[Claude navigates to agencyai.me/bootcamp.html and explains the offering]
User: "Book me for the bootcamp"
Calls book_consultation with service_package=automated_workflows (bootcamp variant)
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:

Homepage (agencyai.me/):
- AI assistant can recommend services based on site content
- MCP provides detailed service specs and pricing
Bootcamp (agencyai.me/bootcamp.html):
- AI assistant can assess if bootcamp is right fit
- MCP provides alternative service recommendations
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

Agency AI MCP Server

Agency AI MCP Server

🎯 What This Is

🚀 Why MCP Matters

📋 Available Tools

1. get_service_offerings

2. assess_ai_readiness

3. book_consultation

🔗 HTTP API (For Direct Integration)

POST /api/recommend-services

POST /api/assess-readiness

POST /api/book-consultation

🔧 Architecture

📁 Project Structure

🚀 Getting Started

For AI Assistant Users

For Developers

🧪 Testing

📊 Client Journey Examples

Journey 1: The Discovery Flow

Journey 2: The Expert Flow

Journey 3: The Bootcamp Flow

💡 Business Value for Agency AI

Before MCP

After MCP

🔮 Future Roadmap

Phase 1: Production Booking (Immediate)

Phase 2: Enhanced Intelligence

Phase 3: Full Automation

🔗 Integration with agencyai.me

🛠️ Technical Stack

📝 Configuration

Services Data

Business Logic

Allowed Hosts

📞 Support

📄 License

Reviews

1. `get_service_offerings`

2. `assess_ai_readiness`

3. `book_consultation`

POST `/api/recommend-services`

POST `/api/assess-readiness`

POST `/api/book-consultation`