freshdesk-mcp-connector

name	freshdesk-mcp-connector
description	MCP connector for Freshdesk – enables AI-powered helpdesk management, ticket automation, and knowledge base operations through Claude
license	Proprietary/API
tags	["business","support","mcp","freshdesk","helpdesk"]
difficulty	intermediate
time_to_master	4-8 weeks
version	1.0.0

Freshdesk MCP Connector

Overview

This skill enables Claude to interact with Freshdesk through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Freshdesk's REST API, allowing natural language control of Freshdesk operations, intelligent automation, and AI-powered assistance for Freshdesk workflows.

When to Use This Skill

• Ticket management and assignment via AI
• Automation rule creation
• Solution article generation
• Customer satisfaction tracking
• Agent productivity analysis

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Freshdesk        │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (REST API      )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Freshdesk operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.

Key Endpoints/Interfaces

/api/v2/tickets, /api/v2/contacts, /api/v2/solutions/articles, /api/v2/automations, /api/v2/canned_responses

Implementation

// Freshdesk MCP Server 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: "freshdesk-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Freshdesk 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 }) => {
    // Call Freshdesk REST API
    const response = await fetch(`${BASE_URL}/api/v2/tickets`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Freshdesk",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/api/v2/tickets`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Freshdesk data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}/api/v2/tickets`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "freshdesk-mcp-connector": {
      "command": "node",
      "args": ["path/to/freshdesk-mcp-connector/index.js"],
      "env": {
        "FRESHDESK_API_KEY": "your-api-key",
        "FRESHDESK_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Freshdesk API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Freshdesk API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Create canned responses for the top 10 frequently asked questions and set up auto-assignment rules"

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

• Freshdesk Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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