ERP-lite MCP Server

An enterprise-ready Model Context Protocol (MCP) server that exposes ERP functionalities to AI agents. Built as a portfolio project to demonstrate AI/ML maturity, this project features a realistic data schema and a critical human-in-the-loop workflow for write actions.

Overview

As enterprise AI adoption accelerates, providing LLMs with direct read/write access to ERPs is becoming essential. However, autonomous agents should not execute consequential operations (like creating purchase orders or altering system configurations) without human oversight.

This server demonstrates a robust "human-in-the-loop" pattern:

• The AI agent can query open sales orders, check inventory levels, and identify low-stock items using its read-only tools.

• When an agent decides to replenish stock, it can only propose a purchase requisition in a pending_approval state.

• The agent cannot approve its own requisition. A human must intervene to approve it.

Related MCP server: PyerP MCP Server

Architecture

graph TD
    Client[Claude Desktop / Custom Client] -->|MCP (stdio or SSE HTTP)| FastMCP[FastMCP Server]
    FastMCP -->|SQLAlchemy| DB[(PostgreSQL Database)]
    DB --> Seed[Seed Data]

Quick Start (Docker)

To get started quickly, run the entire stack with Docker Compose:

docker compose up

This spins up:

• A PostgreSQL database (pre-seeded with realistic enterprise data for sales orders, inventory, and requisitions).

• The MCP server exposing SSE transport at http://localhost:8000/sse.

Tools Exposed

• get_open_orders(status="open", limit=20): Retrieves a list of sales orders by status.

• check_inventory(material_id): Checks the inventory level and computes if it's below the reorder point.

• get_low_stock_items(): Intelligent query that identifies all inventory items below their reorder threshold.

• create_requisition(material_id, quantity, requested_by): WRITE TOOL. Creates a new purchase requisition in a pending_approval state.

• approve_pending_requisition(requisition_id, approved_by): WRITE TOOL. Approves a pending requisition. Must be explicitly triggered by human confirmation.

Testing Locally

Using the Custom Python Client

To prove that this server supports remote transport via HTTP (Server-Sent Events), you can use the built-in client script:

python client.py

Using Claude Desktop (Stdio transport)

To test with Claude Desktop, configure your claude_desktop_config.json to use the uv run command:

{
  "mcpServers": {
    "erp-lite": {
      "command": "uv",
      "args": [
        "run",
        "python",
        "-m",
        "src.server"
      ]
    }
  }
}

Running Unit Tests

To run the pytest suite testing the tool logic and requisition lifecycle:

uv run pytest

Future Enhancements

• Authentication & RBAC: Implement Role-Based Access Control to ensure the approved_by identity has the actual rights to approve the specific value/material in the requisition.

• Audit Logging: Maintain a strict append-only audit trail of who/what queried and executed which tool, crucial for compliance (e.g. SOX).

• Policy Search Resource: A RAG-like capability to expose procurement policy documents to the agent as MCP resources.