Customer Support Server

An intelligent customer support agent powered by Model Context Protocol (MCP) that integrates with CRMs, ticketing systems, communication tools, and databases to provide personalized, real-time assistance.

Features

• Real-time Data Access: Pull current order status, shipping details, account history from databases/APIs via MCP
• Context Awareness: Access previous conversations, tickets, emails, Slack threads through unified memory layer
• Action Capabilities: Update tickets in Zendesk, create new tickets, send notifications instantly
• Context Persistence: Maintain conversation history across channels
• Flexible LLM Support: Choose between Ollama (local, private), OpenAI (cloud, powerful), or Vertex AI/Gemini (Google Cloud)
• MCP Integrations: Secure connections to enterprise systems (CRMs, ERPs, helpdesk tools) in real-time

Architecture

customersupport/
├── agent/              # Main agent application
├── mcp_integrations/   # MCP server integrations
├── integrations/       # External system connectors
├── memory/             # Context persistence layer
├── config/             # Configuration files
├── utils/              # Utility functions
└── frontend/           # React UI for customer support chat

Quick Start

See SETUP.md for detailed installation and configuration instructions.

Basic Setup

0.Create developer accounts for Zendesk and Slack https://ekluvtech.com/2026/02/21/creating-developer-accounts-for-zendesk-and-slack/

1. Install dependencies:

  #windows
  python3  -m  venv custsupport
  .\custsupport\Scripts\activate

  #Mac
  python3.10 -m venv custsupport
  source custsupport/bin/activate

pip install -r requirements.txt

2. Create developer accounts

• Zendesk trial or dev/sandbox account
• Slack app in a development workspace
• Set up an order database(PostgreSQL/MySQL for production-like testing)
• Choose an LLM provider (OpenAI, Anthropic, Geminietc.) and get an API key
• export all the required environment varaibles
• export ZENDESK_SUBDOMAIN=*********@
• export ZENDESK_EMAIL=*********@gmail.com
• export ZENDESK_API_KEY=***************************
• export SLACK_TOKEN=xoxp--10255901183920--9301425075be6f3170e95241c7
• export DATABASE_URL="postgresql://ordruser:Admin123@localhost/ordrmgmnt"

2. Configure the agent:

   cp config/config.example.yaml config/config.yaml
   # Edit config/config.yaml with your settings
   

3. Configure LLM Provider:

   The agent supports three LLM providers:

   Option A: Ollama (Local, Private)

   • Start Ollama (if not already running):

     ollama pull llama2
     

   • Configure in config/config.yaml:

     llm:
       provider: ollama
     

   Option B: OpenAI (Cloud, Powerful)

   • Set your OpenAI API key:

     export OPENAI_API_KEY=your-api-key-here
     

   • Configure in config/config.yaml:

     llm:
       provider: openai
     openai:
       api_key: "${OPENAI_API_KEY}"
       model: gpt-4
     

   Option C: Vertex AI / Gemini (Google Cloud)

   • Option 1: Service Account JSON (Recommended)
     • Create a service account in Google Cloud Console
     • Download the JSON key file
     • Set credentials path:

       export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json
       export GOOGLE_CLOUD_PROJECT=your-project-id
       

   • Install Vertex AI dependencies:

     pip install google-cloud-aiplatform
     

   • Configure in config/config.yaml:

     llm:
       provider: vertexai  # or "gemini"
     vertexai:
       credentials_path: "${GOOGLE_APPLICATION_CREDENTIALS}"  # For JSON file
       project_id: "${GOOGLE_CLOUD_PROJECT}"
       location: us-central1
       model: gemini-pro
     

   • See GOOGLE_CLOUD_SETUP.md for detailed setup instructions
   • See VERTEX_AI_TROUBLESHOOTING.md if you encounter rate limit errors (429)

4. Run MCP server:

  `python -m mcp_integrations.unified_server --http --port 8000` (running all the tools under unified server)

5. Start the API server:

   python -m agent.api
   

6. Modern chat interface for customers:

   python -m agent.api
   ``
   

#if you get any certificate issue while connection to slack pip install --upgrade certifi export SSL_CERT_FILE=$(python -m certifi)

Usage

The agent can be used via:

• Web UI (React): Modern chat interface for customers (see Frontend Setup)
• Interactive Chat: python -m examples.interactive_chat
• MCP Server: python -m mcp_integrations.unified_server --http --port 8000 (running all the tools under unified server)
• REST API: python -m agent.api (requires FastAPI/uvicorn)
• Programmatic: Import and use the SupportAgent class directly

Frontend Setup

1. Navigate to frontend directory:

   cd frontend
   

2. Install dependencies:

   npm install
   

3. Start the development server:

   npm run dev
   

   The React app will open at http://localhost:3000

4. Make sure the MCP Server is running:

   # In a separate terminal
    python -m mcp_integrations.unified_server --http --port 8000
   

   The MCP Server runs on http://localhost:8000/ by default. and you can verify the tools available by querying http://localhost:8000/tools

5. Make sure the backend API is running:

   # In a separate terminal
   python -m agent.api
   

   The API runs on http://localhost:8100 by default.

6. Start chatting! The UI will automatically connect to the backend API.

For more details, see frontend/README.md.

Demo Data

To populate Zendesk, Salesforce, Slack, and your database with sample data for testing:

1. See demo_data/README.md for detailed instructions
2. Set up environment variables for each service
3. Run the population scripts:

   # Populate all services
   python demo_data/populate_all.py
   
   # Or populate individually:
   python demo_data/populate_database.py
   python demo_data/populate_zendesk.py
   python demo_data/populate_salesforce.py
   python demo_data/populate_slack.py
   

This will create sample tickets, orders, cases, and messages that you can use to test the agent's capabilities.

Privacy & Security

• Ollama (Local): All data processed locally, no data leaves customer premises
• OpenAI (Cloud): Data is sent to OpenAI's API - review OpenAI's privacy policy
• Vertex AI (Google Cloud): Data is sent to Google Cloud - review Google Cloud's privacy policy
• Secure MCP connections with authentication
• Customer identity verification before accessing sensitive data
• Configurable encryption and data retention policies

License

MIT