| name | zendesk-mcp-connector |
| description | MCP connector for Zendesk – enables AI-powered ticket management, customer support automation, and help desk operations through Claude |
| license | Proprietary/API |
| tags | ["business","support","mcp","zendesk","helpdesk"] |
| difficulty | intermediate |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
Zendesk MCP Connector
Overview
This skill enables Claude to interact with Zendesk through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Zendesk's REST API, allowing natural language control of Zendesk operations, intelligent automation, and AI-powered assistance for Zendesk workflows.
When to Use This Skill
- Ticket triage and priority assignment via AI
- Macro and automation creation
- Knowledge base article generation
- Customer satisfaction analysis
- SLA monitoring and reporting
Architecture
┌─────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ Claude │────▶│ MCP Server │────▶│ Zendesk │
│ (Client) │◀────│ (TypeScript) │◀────│ (REST API )│
└─────────────┘ └─────────────────┘ └──────────────────┘
Core Concepts
MCP Server Setup
The connector implements an MCP server that exposes Zendesk operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.
Key Endpoints/Interfaces
/api/v2/tickets, /api/v2/users, /api/v2/organizations, /api/v2/macros, /api/v2/automations
Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
const server = new McpServer({
name: "zendesk-mcp-connector",
version: "1.0.0",
});
server.tool(
"list_resources",
"List and query Zendesk resources with optional filters",
{
query: z.string().optional().describe("Search query or filter"),
limit: z.number().optional().describe("Max results to return"),
},
async ({ query, limit }) => {
const response = await fetch(`${BASE_URL}/api/v2/tickets`, {
headers: { "Authorization": `Bearer ${API_KEY}` },
});
const data = await response.json();
return {
: [{ : , : .(data, , ) }],
};
}
);
server.(
,
,
{
: z.().(),
: z.({}).().().(),
},
({ name, config }) => {
response = (, {
: ,
: {
: ,
: ,
},
: .({ name, ...config }),
});
data = response.();
{
: [{ : , : }],
};
}
);
server.(
,
,
{
: z.().(),
: z.().().(),
},
({ , timeframe }) => {
response = (, {
: { : },
});
data = response.();
{
: [{ : , : .(data, , ) }],
};
}
);
transport = ();
server.(transport);
Claude Desktop Configuration
{
"mcpServers": {
"zendesk-mcp-connector": {
"command": "node",
"args": ["path/to/zendesk-mcp-connector/index.js"],
"env": {
"ZENDESK_API_KEY": "your-api-key",
"ZENDESK_BASE_URL": "https://your-instance-url"
}
}
}
}
Best Practices
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Zendesk API rate limits
- Error Handling: Provide clear, actionable error messages for common failure scenarios
- Pagination: Handle paginated responses for large datasets efficiently
- Caching: Cache frequently accessed read-only data to reduce API calls
- Security: Validate all inputs before passing to the Zendesk API; sanitize outputs
- Logging: Log all API interactions for debugging and audit purposes
Example Prompts
"Triage all open tickets, assign priorities based on content, and draft responses for common issues"
Security Considerations
- All API credentials must be stored as environment variables
- Implement input validation and sanitization for all tool parameters
- Use HTTPS for all API communications
- Follow the principle of least privilege for API token permissions
- Audit log all write operations for compliance tracking
Resources
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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