ERPNext MCP Server

The Erpnext MCP Server bridges the gap between ERPNext’s rich business logic and modern AI assistants by exposing a standardized Model Context Protocol (MCP) interface. It turns ERPNext into an intelligent data source that Claude and other AI agents can query, manipulate, and orchestrate with minimal friction. By providing a single entry point for management operations, file handling, read‑only database reads, and direct API calls, the server enables developers to embed business workflows directly into conversational agents without writing custom adapters for each endpoint.

At its core, the server implements a set of MCP resources that mirror ERPNext’s document model. These resources allow agents to perform CRUD operations, run server‑side scripts, and access file storage—all while respecting ERPNext’s permission system. The accompanying tools layer supplies reusable actions such as “create invoice,” “upload attachment,” or “fetch sales order status.” Prompts are pre‑configured to translate natural language into the appropriate resource calls, reducing the cognitive load on users and ensuring consistent request patterns. The server’s sampling capabilities enable agents to retrieve paginated datasets or filtered queries, which is essential for handling large inventories or customer lists.

Developers benefit from a tightly integrated workflow that mirrors Frappe’s own deployment patterns. During local development, the server runs on port 8080 with hot‑reloading and live debugging, allowing rapid iteration. When ready for production, a single command configures NGINX to serve the MCP under on port 8100 and supervises the process, guaranteeing high availability. This parity between development and production eliminates “works‑on-my-machine” surprises and streamlines continuous delivery pipelines.

Real‑world scenarios that gain from this MCP server include automated order processing, where an AI assistant can pull customer data, generate invoices, and trigger shipment workflows—all within a single conversational turn. Customer support bots can retrieve ticket histories or update status fields without leaving the chat interface, improving response times and data consistency. Internal knowledge bases can be enriched by allowing agents to query ERPNext’s documentation, schedules, or asset records on demand. In each case, the server abstracts away REST endpoints and authentication details, letting developers focus on intent modeling rather than plumbing.

Unique to the Erpnext MCP Server is its adherence to Frappe’s established patterns, ensuring that any ERPNext instance—whether a small business or an enterprise deployment—can adopt the server without compromising existing infrastructure. The framework’s configuration commands provide a declarative approach to environment management, while the clear separation of tools, resources, and prompts encourages modularity. Together, these features make the server a powerful enabler for AI‑driven automation across ERPNext’s ecosystem.