Logistics FastMCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Logistics FastMCP ServerInvestigate shipment SHP-7701 - get status, carrier events, and relevant SOP."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
LogiLink
LogiLink serves as a practical use case demonstrating how the Model Context Protocol (MCP) and Agent-to-Agent (A2A) collaboration patterns solve complex, mission-critical enterprise workflows.
When cargo is delayed, damaged, or held at customs, LogiLink autonomously coordinates specialist agents over A2A and interfaces with backend telemetry and systems via MCP to investigate carrier status, query real-time IoT telematics, retrieve SOPs (Standard Operating Procedures), evaluate regional inventory buffers, file carrier SLA (Service Level Agreement) liability claims, and synthesize actionable operational resolutions.

Multi-Agent Architecture & Protocol Separation
LogiLink enforces clean architectural decoupling using two open industry standards: Google A2A (Agent-to-Agent) for inter-agent communication and Model Context Protocol (FastMCP) for standardized tool execution and data access.
System Topology & Protocol Decoupling
flowchart TD
UI["LogiLink Mission Control Cockpit"]
subgraph Orchestration["Agent-to-Agent Coordination Layer"]
COORD["Coordinator Orchestrator<br/><i>(smolagents + Ollama LLM)</i>"]
end
subgraph Specialists["Specialist Agent Swarm (Google A2A Protocol)"]
direction LR
SHIP["Shipment Agent<br/><b>Port :8101</b><br/><i>Telemetry & Anomaly Diagnosis</i>"]
KNOW["Knowledge Agent<br/><b>Port :8102</b><br/><i>SOP Retrieval & Vector Search</i>"]
INV["Inventory Agent<br/><b>Port :8103</b><br/><i>Safety Stock & DC Allocation</i>"]
ESC["Escalation Agent<br/><b>Port :8104</b><br/><i>IoT Telematics & Carrier Claims</i>"]
end
subgraph Tooling["Model Context Protocol (FastMCP)"]
MCP["Logistics FastMCP Server<br/><i>(Tool Execution Engine)</i>"]
end
subgraph Storage["Persistent Logistics Data Layer"]
direction LR
D_SHIP[("Shipment Status &<br/>Carrier Scans")]
D_SOP[("Markdown SOP<br/>Catalog")]
D_INV[("Regional Warehouses &<br/>Safety Stock")]
D_IOT[("IoT Sensor Telemetry &<br/>Carrier Contracts")]
end
UI -->|REST / Run Scenario| COORD
COORD -->|A2A JSON-RPC :8101| SHIP
COORD -->|A2A JSON-RPC :8102| KNOW
COORD -->|A2A JSON-RPC :8103| INV
COORD -->|A2A JSON-RPC :8104| ESC
SHIP -->|FastMCP Tool Calls| MCP
KNOW -->|FastMCP Tool Calls| MCP
INV -->|FastMCP Tool Calls| MCP
ESC -->|FastMCP Tool Calls| MCP
MCP --> D_SHIP
MCP --> D_SOP
MCP --> D_INV
MCP --> D_IOT
COORD -.->|Live SSE Stream / Real-time Status| UI
classDef ui fill:#1e293b,stroke:#38bdf8,stroke-width:2px,color:#f8fafc;
classDef coord fill:#0f172a,stroke:#818cf8,stroke-width:2px,color:#f8fafc;
classDef specialist fill:#1e293b,stroke:#34d399,stroke-width:1.5px,color:#f8fafc;
classDef mcp fill:#1e293b,stroke:#f59e0b,stroke-width:2px,color:#f8fafc;
classDef data fill:#0f172a,stroke:#94a3b8,stroke-width:1px,color:#cbd5e1;
class UI ui;
class COORD coord;
class SHIP,KNOW,INV,ESC specialist;
class MCP mcp;
class D_SHIP,D_SOP,D_INV,D_IOT data;Incident Resolution Lifecycle & Execution Flow
sequenceDiagram
autonumber
actor Operator as Logistics Operator / UI
participant Coord as Coordinator Agent
participant Ship as Shipment Agent (:8101)
participant Know as Knowledge Agent (:8102)
participant Inv as Inventory Agent (:8103)
participant Esc as Escalation Agent (:8104)
participant MCP as Logistics FastMCP Server
Operator->>Coord: Trigger Incident Investigation (e.g. SHP-7701)
rect rgb(30, 41, 59)
Note over Coord,Ship: Phase 1: Anomaly Diagnosis
Coord->>Ship: A2A Request: Diagnose Status & Timeline
Ship->>MCP: Call get_shipment_status & get_carrier_events
MCP-->>Ship: Telemetry Data (Status: CRITICAL_ALERT)
Ship->>MCP: Call analyse_shipment_events
MCP-->>Ship: Deterministic Issue: TEMPERATURE_EXCURSION
Ship-->>Coord: A2A Response: Diagnosed Issue & Evidence
end
rect rgb(30, 41, 59)
Note over Coord,Know: Phase 2: SOP Compliance Matching
Coord->>Know: A2A Request: Retrieve Remediation Protocol
Know->>MCP: Call search_sop(TEMPERATURE_EXCURSION)
MCP-->>Know: SOP Markdown: cold_chain_excursion
Know-->>Coord: A2A Response: Matched Actions & Guidelines
end
rect rgb(30, 41, 59)
Note over Coord,Inv: Phase 3: Warehouse Buffer Allocation
Coord->>Inv: A2A Request: Check Buffer & Reserve Replacement
Inv->>MCP: Call query_warehouse_inventory(SKU-PHARM-770)
MCP-->>Inv: Stock Levels (Berlin DC: 14 units available)
Inv->>MCP: Call reserve_replacement_stock(quantity=1)
MCP-->>Inv: Reservation Confirmed (RES-EEBF8425)
Inv-->>Coord: A2A Response: Buffer Reserved & DC Details
end
rect rgb(30, 41, 59)
Note over Coord,Esc: Phase 4: SLA Claim & Carrier Ticket
Coord->>Esc: A2A Request: Inspect IoT & Escalate Claim
Esc->>MCP: Call get_iot_telemetry(SHP-7701)
MCP-->>Esc: Sensor Log (14.2°C Excursion >30min in Bay 4)
Esc->>MCP: Call generate_carrier_ticket(SLA breach, Priority: HIGH)
MCP-->>Esc: Ticket Created (ESC-516629, Penalty Clause active)
Esc-->>Coord: A2A Response: Ticket Details & Carrier Desk
end
Coord->>Operator: Synthesized Operational Resolution (Markdown)Related MCP server: ShipSmart MCP Server
Quickstart
1. Prerequisites
Python 3.11+
Node.js 18+ (for frontend build)
uv (recommended package manager)
Ollama running locally with a model (e.g.
gpt-oss:120b-cloud,qwen3.5:9b)
2. Installation & Frontend Build
Step A: Setup Backend Environment
# Clone the repository
git clone https://github.com/CagriCatik/LogiLink.git
cd LogiLink
# Create virtual environment & install Python dependencies
uv venv .venv
uv pip install --python .venv -e ".[dev]"Step B: Install & Build Frontend
The LogiLink cockpit requires building the React + XYFlow production bundle into frontend/dist/ (which FastAPI automatically serves):
cd frontend
npm install
npm run build
cd ..Note for Frontend Developers: To run the frontend in live HMR development mode (port
5173) with API proxying to port8006, execute:cd frontend npm run dev
3. Launch LogiLink Cockpit
# Start the Visual Cockpit on http://localhost:8006
python main.pyOpen your browser at http://localhost:8006 to access the LogiLink Mission Control canvas.
4. Run CLI Interactive Chat (Optional)
python main.py --mode=chat --model="gpt-oss:120b-cloud"Testing & Verification Suite
LogiLink includes automated test coverage verifying all domain logic, MCP tools, and agent workflows:
1. Run Automated E2E Scenario Test Suite
Tests all 5 operational scenarios against the multi-agent coordinator and validates sequence diagnosis, SOP matching, warehouse allocation, and escalation ticketing:
python scratch/test_all_scenarios_e2e.py2. Run Headless Playwright UI Verification
Validates the React XYFlow Cockpit in a headless browser, tests node clicks, checks payload rendering, and validates zero emojis across the DOM:
python scratch/verify_all_scenarios_ui.py3. Run Unit and Contract Tests
pytestThis server cannot be deployed
Maintenance
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