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name: erpnext-mcp-connector description: "MCP connector for ERPNext – enables AI-powered business management, workflow automation, and reporting through Claude" license: GPL tags: ["business","erp","mcp","erpnext","frappe"] difficulty: advanced time_to_master: "4-8 weeks" version: "1.0.0"

Erpnext MCP Connector

Overview

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

When to Use This Skill

• Document creation and workflow management via AI
• Financial report generation and analysis
• Supply chain and inventory management
• HR module operations and payroll processing
• Custom doctype and script development

Architecture

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

Core Concepts

MCP Server Setup

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

Key Endpoints/Interfaces

/api/resource/:doctype, /api/method/:method, /api/resource/:doctype/:name, /api/method/frappe.client.*

Implementation

// Erpnext 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: "erpnext-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Erpnext 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 Erpnext REST API (Frappe)
    const response = await fetch(`${BASE_URL}/api/resource/:doctype`, {
      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 Erpnext",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/api/resource/:doctype`, {
      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 Erpnext 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/resource/:doctype`, {
      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": {
    "erpnext-mcp-connector": {
      "command": "node",
      "args": ["path/to/erpnext-mcp-connector/index.js"],
      "env": {
        "ERPNEXT_API_KEY": "your-api-key",
        "ERPNEXT_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 Erpnext 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 Erpnext API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Generate a monthly P&L report and identify the top 3 expense categories with year-over-year growth"

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

• Erpnext 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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