Zendesk MCP Server

Zendesk MCP Server (Model Context Protocol)

This project is a lightweight, AI-native MCP (Model Context Protocol) server that integrates with Zendesk's REST APIs. It allows GPT-based AI agents (e.g. OpenAI, LangChain) to fetch real-time customer and organization context dynamically.

Features

Accepts ticket_id, user_id, or organization_id
Fetches user, org, and ticket context from Zendesk
Returns:
- summary: human-readable LLM-friendly summary
- prompt_context: single-line LLM embedding string
- context: structured blocks (text, list)
- prompt_guidance: usage instructions and few-shot examples
Exposes:
- /context: main context API
- /meta: MCP schema metadata
- /function-schema: OpenAI function-compatible definition
Fully Dockerized and deployable
Compatible with GPT-4 function calling

Getting Started

1. Clone and install dependencies

git clone https://github.com/your-repo/zendesk-mcp-server
cd zendesk-mcp-server
npm install

2. Set up `.env`

ZENDESK_DOMAIN=your-subdomain.zendesk.com
ZENDESK_EMAIL=your-email@yourdomain.com
ZENDESK_API_TOKEN=your_zendesk_api_token
PORT=3000

3. Run Locally

node index.js

Visit:

http://localhost:3000/context
http://localhost:3000/meta
http://localhost:3000/function-schema

Docker Support

Build Image

docker build -t zendesk-mcp .

Run Container

docker run -p 3000:3000 \
  -e ZENDESK_DOMAIN=your-subdomain.zendesk.com \
  -e ZENDESK_EMAIL=your-email \
  -e ZENDESK_API_TOKEN=your-token \
  zendesk-mcp

Function Calling with OpenAI (Example)

See openai-client.js for an example where:

GPT-4 automatically detects and calls get_ticket_context
The function calls your local MCP server
GPT writes a natural reply using the returned context

Simulating a Full Chat Conversation

What you've tested so far is GPT-4 calling your MCP server using function calling, which works. Now you want to simulate a full conversation where:

A user asks something natural like:

“Can you give me context for ticket 12345?”

GPT-4 figures out it needs to call get_ticket_context
GPT-4 calls your MCP server automatically
GPT-4 uses the result to reply in a natural, chat-style response

Let’s build exactly that — your own OpenAI Agent Loop that mimics how GPT-4 with tools (functions) will behave in production.

✅ Step-by-Step: Full Chat-Based OpenAI Agent with Function Calling

✨ Final Output Looks Like:

User: Can you give me context for ticket 12345?
GPT: Sure! Here's what I found:

Alice Smith is a Premium customer under Acme Corp. She submitted 3 tickets recently. The latest ticket is titled "Login timeout" and is currently open.

What This Script Does:

Sends a natural user message to GPT-4
GPT-4 detects your function, calls it with a ticket_id
You send that to your MCP server
Feed the MCP server’s context result back to GPT
GPT-4 writes a human-style response using the result

Web Chat Interface + OpenAI Router API

To demonstrate end-to-end usage with real input/output, this project includes:

1. `/chat` API endpoint (`openai-router.js`)

A Node.js API that accepts natural language messages, detects intent using GPT-4 + function calling, and uses the MCP server to fetch data and compose replies.

🔧 .env additions:

OPENAI_API_KEY=your_openai_key
MCP_SERVER_URL=http://localhost:3000
CHAT_PORT=4000

▶️ Run the API:

node openai-router.js

This starts a server at http://localhost:4000/chat

2. `chat-ui.html`

A simple HTML frontend to type user prompts and see AI-generated responses with Zendesk context.

🧪 Example Questions:

Who is the user for ticket 12345?
Tell me about organization 78901
How many tickets has user 112233 opened?

💬 Usage

Open chat-ui.html in a browser
Ensure the /chat endpoint is running with CORS enabled
Ask questions and see the result appear naturally

🔐 Note

Make sure you install and enable CORS in openai-router.js:

const cors = require('cors');
app.use(cors());

Future Enhancements

LangChain tool compatibility
Redis caching layer
Rate limiting
More context types: /orders, /billing, /subscriptions

License

MIT

Author

Your Name — @yourhandle

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A lightweight, AI-native server that enables GPT-based AI agents to fetch real-time customer and organization context from Zendesk APIs dynamically.

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