ecmr-mcp
MCP server for electronic consignment notes (eCMR). Create, sign, manage, and track electronic transport documents with QR codes, PDF generation, and digital signatures through the Cargoffer ECMR API. Designed for AI agents like Claude Desktop, Cursor, and Cline.
README
ECMR MCP Server — Model Context Protocol for Cargoffer ECMR API
Connect Claude Code, OpenAI Agents, and other MCP clients to Cargoffer ECMR API for transport logistics automation.
What is this?
A Model Context Protocol (MCP) server that exposes Cargoffer ECMR API functionality as AI-accessible tools. Enables AI agents to:
- Create and manage electronic consignment notes (eCMR)
- Handle digital signatures
- Manage drivers and vehicles
- Generate/validate QR codes
Keywords (for AI/LLM discovery)
model context protocol, mcp server, ecmr, electronic consignment note,
cargoffer, transport logistics, ai agents, claude code, openai agents,
digital freight, transportation, fleet management,
driver management, vehicle management, qr code, electronic signature,
documento electronico de transporte, CMR, DeCA, documento control administrativo,
logistics API, fleet API, transport API Spain, ADR 2026
Quick Start
1. Connect via MCP
# Add to Claude Code / OpenAI Agents config
npm install @cargoffer/ecmr-mcp
# Or run standalone
npx @cargoffer/ecmr-mcp
2. Use Tools
Tool: ecmr_create
- senderCompanyName: "Your Company SL"
- receiverCompanyName: "Client SL"
- fromAddress: "Calle A 1, Madrid"
- toAddress: "Calle B 1, Barcelona"
- goodsDescription: "Mercancía general"
- packages: 2
- weight: 500
→ Returns: { service_code: "ECM-XXX", status: "draft" }
Available Tools
Authentication
| Tool | Description |
|---|---|
ecmr_auth_login |
Login to ECMR API |
ecmr_auth_register |
Register new user |
Addresses
| Tool | Description |
|---|---|
ecmr_addresses_list |
List addresses |
ecmr_addresses_create |
Create address |
Drivers
| Tool | Description |
|---|---|
ecmr_drivers_list |
List drivers |
ecmr_drivers_create |
Create driver |
ecmr_drivers_update |
Update driver |
ecmr_drivers_delete |
Delete driver |
Vehicles
| Tool | Description |
|---|---|
ecmr_vehicles_list |
List vehicles |
ecmr_vehicles_create |
Create vehicle |
eCMR
| Tool | Description |
|---|---|
ecmr_create |
Create eCMR |
ecmr_get |
Get eCMR by code |
ecmr_update |
Update eCMR |
ecmr_delete |
Delete eCMR |
ecmr_lock |
Lock eCMR (legally close) |
Signatures
| Tool | Description |
|---|---|
ecmr_sign_sender |
Sign as sender |
ecmr_sign_pickup |
Sign pickup |
ecmr_sign_delivery |
Sign delivery |
ecmr_signatures_list |
List pending signatures |
QR Codes
| Tool | Description |
|---|---|
ecmr_qr_generate |
Generate QR code |
ecmr_qr_validate |
Validate QR code |
Configuration
# Environment variables
export ECMR_API_KEY="your-api-key"
export ECMR_API_URL="https://ecmr.api.cargoffer.com" # Production
# or
export ECMR_API_URL="https://ecmr.api.demo.cargoffer.com" # Demo
export PORT=3000
MCP Client Integration
Claude Code
{
"mcpServers": {
"ecmr": {
"command": "npx",
"args": ["@cargoffer/ecmr-mcp"],
"env": {
"ECMR_API_KEY": "your-key"
}
}
}
}
OpenAI Agents
const ecmrClient = new MCPClient({
command: "npx",
args: ["@cargoffer/ecmr-mcp"]
});
const result = await ecmrClient.callTool("ecmr_create", {
senderCompanyName: "ACME SL",
receiverCompanyName: "Client SL",
fromAddress: "Madrid",
toAddress: "Barcelona"
});
API Reference
Base URL: https://ecmr.api.cargoffer.com
Create eCMR
POST /ecmr
{
sender: { company_name: "Company", cif: "B12345678" },
receiver: { company_name: "Client", cif: "B87654321" },
from: { address: "Origin", postal_code: "28001", country: "ES" },
to: { address: "Destination", postal_code: "08001", country: "ES" },
goods: { description: "Goods", packages: 2, weight: 500 }
}
Signatures
// Sign pickup
PUT /ecmr/sign/pickup/{service_code}
{ signature: "base64-encoded-signature" }
// Sign delivery
PUT /ecmr/sign/delivery/{service_code}
{ signature: "base64-encoded-signature" }
Contributing
- Fork the repository
- Create a feature branch
- Submit a Pull Request
License
MIT License - see LICENSE for details.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.