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Warehouse Ops MCP Toolkit

Warehouse Ops MCP Toolkit

L2 Case 04 – Agentic SDLC Capstone Project

A secure, agent-friendly Warehouse Operations Toolkit built using MCP (Model Context Protocol), providing inventory search, dock management, exception handling, site-based access control, and confirmation-driven operations.

Overview

This project implements an MCP-based toolkit for warehouse operations.

The solution redesigns the original operations wish-list into a set of focused, agent-friendly tools that support inventory lookup, dock management and exception handling while enforcing site-level security controls.

Related MCP server: Logistics FastMCP Server

Features

Inventory Management

  • Inventory Search

  • Inventory Details

  • Stock Adjustment

  • Bin Transfer

Dock Operations

  • Dock Availability

  • Dock Booking

  • Dock Cancellation

Exception Management

  • Create Exception

  • Resolve Exception

Security

  • Site-based access control

  • Cross-site access prevention

  • Toolkit-enforced authorization

Testing

Automated tests implemented using pytest.

Current Results:

4 passed

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables warehouse operations agents to inspect tasks, deliveries, stock, and KPIs, and to attempt write operations in shadow mode with human approval and segregation of duties enforced as code.
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  • F
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    Provides standardized tools for querying shipment status and carrier events, searching SOPs, checking and reserving warehouse inventory, retrieving IoT telemetry, and generating carrier SLA claim tickets so agents can diagnose and resolve logistics incidents.
    -
  • A
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    quality
    B
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    Enables an AI agent to query a warehouse management system for orders, stock, stalled orders, and audit logs, and to make narrowly scoped writes by changing order status with a required reason or appending notes, with all writes recorded in a full audit trail.
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  • A
    license
    A
    quality
    B
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    Enables AI agents to manage inventory conversationally: list and look up products, check current stock levels, record inbound and outbound movements idempotently, review movement history, and surface items below their minimum threshold. Every figure is grounded in tool calls rather than model estimation, and invalid operations return explicit error codes the agent can reason about.
    5
    MIT