Operational MCP layer for European road transport intelligence

A route optimization MCP server that connects AI agents (Claude Desktop, Cline, Continue, Cursor, etc.) to the TRANSCEND logistics intelligence platform — giving LLMs real-time access to truck route optimization, toll costs, weather, parking, traffic blackspots, and fuel station pricing across Spain and Europe.

What problem does it solve? Truck logistics operators waste hours switching between Google Maps (not truck-aware), toll websites, weather apps, and fuel price spreadsheets. This MCP server consolidates 6 real-time logistics data sources into a single AI-callable interface so agents can plan routes, estimate costs, and assess risks in one step.

What makes it different? Unlike generic mapping APIs, TRANSCEND is purpose-built for European truck logistics API needs — it respects ADR (dangerous goods) restrictions, truck height/width/weight limits, provides per-segment toll breakdowns with fuel and CO₂ costs, identifies dangerous blackspot zones, and locates truck-specific parking. It is the only unified MCP interface for European road logistics infrastructure.

What integration does it enable? Any MCP-compatible client can invoke 12 logistics intelligence tools covering route optimization, toll cost estimation, weather conditions, POI parking, traffic blackspot analysis, and fuel station pricing — all from a single conversation with an LLM.

What does "easy integration" mean concretely? pip install the package, set TRANSCEND_API_KEY, and add one JSON config block to Claude Desktop. No microservice orchestration, no multi-API key management, no custom middleware. Your AI assistant gains instant access to professional European logistics document automation and route intelligence.

Why this MCP exists

European truck route planning is fragmented. Google Maps doesn't know truck height restrictions. Toll calculators are per-country. Weather alerts come from a third source. Fuel prices change hourly. Dispatchers manually juggle 5+ tabs to answer a single question like "what's the safest and cheapest route from Madrid to Frankfurt for a 4m-high refrigerated truck?"

This MCP server aggregates six TRANSCEND API microservices (Route, Tolls, Weather, POI, Traffic, Stations) behind a single MCP interface — making European road transport data accessible to AI agents through natural language. It transforms the agent from a chat interface into an operational logistics copilot.

Related MCP server: bolsa-de-carga-mcp

MCP Capabilities

• 12 MCP tools across 6 logistics modules — organized by domain, not buried in code

• Truck-aware route optimization with ADR class restrictions, vehicle dimensions, and weight limits

• Full cost breakdown — tolls + fuel consumption + maintenance + CO₂ emissions per route

• Real-time weather — current conditions, multi-day forecasts, and active weather alerts along a corridor

• Truck parking POI search — by coordinates, zip code, or province (with capacity and amenities)

• Traffic blackspot identification — dangerous road zones along a planned route

• Fuel station finder — nearby stations, filtered search, and best-price ranking

• Environment-aware configuration — separate URLs for production vs. dev/release environments

• HTTP/SSE and stdio transports — works with all MCP clients out of the box

• Pydantic-validated inputs and outputs — Python 3.10+, httpx async client

Tools

Directory structure

transcend-mcp-server/
├── pyproject.toml                  # Python 3.10+, dependencies: mcp, httpx, pydantic
├── src/transcend_mcp/
│   ├── server.py                   # FastMCP server bootstrap (stdio / HTTP/SSE)
│   ├── client.py                   # Multi-service HTTP client (6 backend APIs)
│   ├── config.py                   # Environment-aware URL resolution (prod / release-dev)
│   ├── tools.py                    # 12 MCP tool definitions with pydantic schemas
│   ├── __init__.py                 # Package init
│   └── __main__.py                 # Entry point for `python -m transcend_mcp`
└── tests/
    └── test_client.py              # Integration tests for client.py

Authentication

To use this MCP server, you need an API key from the TRANSCEND platform.

Register: https://transcend.cargoffer.com

Create an account and get your API key from your profile settings. Then configure:

export TRANSCEND_API_KEY="your-api-key"

Or in your MCP client config:

{
  "mcpServers": {
    "transcend": {
      "command": "...",
      "env": {
        "TRANSCEND_API_KEY": "your-api-key-from-transcend"
      }
    }
  }
}

Quick Start

git clone https://github.com/cargoffer/transcend-mcp-server.git
cd transcend-mcp-server
pip install -e .
export TRANSCEND_API_KEY="your-api-key"
python -m transcend_mcp

By default runs on stdio (MCP transport). Set MCP_HOST / MCP_PORT for HTTP/SSE mode.

Installation

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "transcend": {
      "command": "python",
      "args": ["-m", "transcend_mcp"],
      "env": {
        "TRANSCEND_API_KEY": "your-api-key",
        "TRANSCEND_ENV": "production"
      }
    }
  }
}

Cursor

Cursor Settings → Features → MCP Servers → Add New:

Name:   transcend
Type:   command
Command: python -m transcend_mcp
Env:    TRANSCEND_API_KEY=your-api-key, TRANSCEND_ENV=production

Direct HTTP / SSE

export MCP_HOST=0.0.0.0
export MCP_PORT=8100
python -m transcend_mcp

# Then connect SSE at http://localhost:8100/sse

MCP protocol in action

Calculate an optimized truck route:

# With stdio transport, use any MCP client. For HTTP/SSE mode:
# First, connect to SSE endpoint, then send JSON-RPC messages

# Example using the JSON-RPC format over SSE (after establishing connection):
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "calculate_route",
  "params": {
    "origin": "Vigo, Spain",
    "destination": "Barcelona, Spain",
    "dimensions": {
      "height": 4.0,
      "width": 2.55,
      "weight": 40000,
      "adr_class": null
    }
  }
}

Get route costs with real toll data:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "calculate_route_costs",
  "params": {
    "route_id": "route_abc_123",
    "fuel_consumption": 35,
    "fuel_type": "diesel",
    "include_co2": true
  }
}

Find truck parking along the route:

{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "find_parking_by_location",
  "params": {
    "latitude": 41.6488,
    "longitude": -0.8891,
    "radius_km": 10
  }
}

Example Prompts for AI Agents

"Calculate an optimized truck route from Vigo to Barcelona. The truck is 4m high, 2.55m wide, 40 tons. Include blackspot warnings."

"What's the total cost of a 15-ton load from Madrid to Valencia with average fuel consumption of 35L/100km using diesel?"

"Find truck parking near Plaza España in Zaragoza."

"What fuel stations are near the A-2 highway at Guadalajara? Show me the best prices."

"Are there any dangerous zones on the route from Seville to Málaga?"

"What's the weather forecast along the A-3 from Madrid to Valencia for tomorrow? Any alerts?"

Configuration

Environment Variables

Production Microservice URLs

Development

pip install -e ".[dev]"
pytest
ruff check src/
ruff format src/

Semantic tags

route optimization MCP truck logistics API MCP server Model Context Protocol European road transport logistics document automation TRANSCEND truck routing toll calculator fuel prices truck parking weather alerts traffic blackspots ADR restrictions CO2 emissions AI agents LLM tools supply chain Spain logistics Cargoffer easy MCP integration

License

MIT — see LICENSE file for details.

Links

• Repository: github.com/cargoffer/transcend-mcp-server

• Issues: github.com/cargoffer/transcend-mcp-server/issues

• MCP Protocol: modelcontextprotocol.io