An Intelligence Briefing for the Port of Rotterdam, from a Single Prompt

Rotterdam is the largest port in Europe. Around 27,000 seagoing vessels call there every year — tankers full of crude, container ships stacked high, ro-ro ferries shuttling trucks between North Sea ports. One seagoing arrival or departure roughly every 19 minutes, around the clock.

LLMs generating intelligence reports that should be based on actual data usually make me slightly nervous. The hallucination problem is real — if a model invents an IMO number or fabricates an inspection record, the output is worse than useless. But the appeal is hard to ignore: describe what you need in plain English and get a structured briefing in seconds, without writing queries or stitching together API responses manually.

So I wanted to test the boundary — what happens when the model isn't generating facts from memory, but pulling them from live databases through tool calls? Does grounding it in real data actually solve the trust problem, or just hide it?

The Setup

Without extra tooling, a language model can only work with what it already knows — training data that's static, incomplete, and difficult to trace back to primary sources. MCP (Model Context Protocol) is one way to close that gap: a standard protocol that lets the model call external APIs during a conversation, so it retrieves data rather than reproducing it from training data.

On the data side, VesselAPI aggregates AIS position data, EU MRV emissions reports, Paris MoU port state inspections, and vessel registry information into a single REST API. The VesselAPI team has wrapped it in an MCP server (vesselapi-mcp on npm) so any MCP-compatible AI client can use it directly.

Configuration is minimal. If you're using an MCP-compatible client, add a .mcp.json file to your project root (requires Node.js 18+):

{
  "mcpServers": {
    "vesselapi": {
      "command": "npx",
      "args": ["-y", "vesselapi-mcp"],
      "env": {
        "VESSELAPI_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

That gives the model access to 16 maritime data tools: vessel search, port lookup, emissions records, inspections, NAVTEX warnings, and more. The MCP server works with any compatible client (Claude Code, Claude Desktop, Cursor, or your own integration).

The Prompt

This was the full prompt:

Generate a morning intelligence briefing for the Port of Rotterdam. Include current vessel traffic, fleet details, EU MRV emissions data, and port state inspection records. Flag anything unusual.

What Actually Happened

The model broke the request into a research plan and worked through it autonomously — a series of tool calls across 7 distinct API endpoints:

1. search_ports("Rotterdam") — found Rotterdam's UN/LOCODE: NLRTM
2. get_port("NLRTM") — pulled port infrastructure details (channel depth, pilotage requirements, repair capabilities)
3. get_port_events("NLRTM") — retrieved the 20 most recent vessel movements
4. get_vessel() — enriched each IMO-registered vessel from the traffic snapshot (type, flag state, tonnage, builder, class society)
5. get_vessel_emissions() — pulled EU MRV emissions records (annual CO2, fuel consumption, efficiency indices)
6. get_vessel_inspections() — pulled port state control inspection histories (deficiencies, detentions, inspection types)
7. get_navtex_messages() — checked for active maritime safety warnings in the broadcast cycle

The model also parallelized where it could — multiple vessel enrichment calls in a single turn, executed concurrently by the MCP client. It applied the same parallel dispatch to emissions and inspection lookups. The model determines what can run concurrently; the client handles execution. Results will vary depending on current traffic and how the model interprets the prompt.

The Briefing

This is the structured report the model produced from those API calls:

Traffic

The API returned the 20 most recent vessel movements at time of query, spanning a roughly two-hour window (18:11–19:59 UTC) — 10 arrivals, 10 departures. This is one page of results; the actual volume at a port this size is considerably higher, and the model could paginate for more. Of these 20, 4 were seagoing vessels. Another 2 were inland commercial vessels (a bunkership and a river tanker), and the remaining 14 were pilot boats, service craft, and inland waterway traffic — the operational layer that keeps a major port functioning.

The four seagoing vessels:

The two inland commercial vessels — SOVEREIGN (IMO 9367023, 5,552 DWT, likely an inland motor tanker based on its home port of Ridderkerk and builder profile) and IMMUNITY (IMO 9316490, 2,934 DWT, Belgian-flagged, classified as a bunkership based on its builder and operating pattern) — are a reminder that Rotterdam's throughput depends heavily on its inland waterway connections. These vessels don't make the headlines, but they move a significant share of the cargo.

Of the four seagoing vessels, two are tankers — chemical and oil/chemical carriers — consistent with Rotterdam's role as Europe's primary energy and petrochemical hub. The two ro-ro vessels (STENA FORETELLER and THULELAND) are part of the short-sea network connecting Rotterdam to UK and Scandinavian ports.

Emissions

Three of the four seagoing vessels have EU MRV data available — the mandatory Monitoring, Reporting, and Verification framework for ships calling at EU ports. The EEXI and EEDI values in the table are design-based energy efficiency indices, calculated at reference conditions and expressed in gCO2 per tonne-nautical mile (lower is better). EEXI applies to existing ships (mandatory from January 2023); EEDI to new ships with building contracts from 2013, subject to size and vessel-type thresholds.

*STENA FORETELLER reported only 1,292t CO2 in 2023 across just 131 sea hours, suggesting the vessel was largely inactive that year rather than a real emissions increase.

The year-over-year CO2 reductions for THULELAND (-43%) and CHEMICAL HUNTER (-32%) need context. THULELAND's sea hours dropped about 7%, while CHEMICAL HUNTER's fell roughly 14%. Some of the reduction reflects fewer voyages or different routes. THULELAND's fuel consumption dropped 43% against only a 7% reduction in sea time — too large to explain by voyage count alone, and likely reflects a significant change in operational pattern between the two years, whether route mix, cargo profile, or periods of reduced activity.

These are the kinds of distinctions that matter under CII (Carbon Intensity Indicator) ratings, which have been mandatory since January 2023. Absolute CO2 measures total emissions; attained CII normalises for transport work done, making it a better measure of operational efficiency. With FuelEU Maritime in force from January 2025 and EU ETS covering maritime emissions, these numbers have direct financial weight. EU ETS for shipping covers 100% of intra-EU voyage emissions and 50% of voyages into or out of the EU, with a phase-in of 40% of verified emissions in 2024, 70% in 2025, and 100% from 2026. At roughly €65–70 per tonne of CO2, STENA FORETELLER's 2024 ETS liability at the 40% phase-in rate could be in the range of €580K for fully intra-EU operations — lower if a significant share of voyages are extra-EU (e.g., Rotterdam–UK), which attract the 50% rate.

Combined, the three vessels emitted 43,012 tonnes of CO2 in 2024. Of that, 2,231 tonnes were at-berth emissions from STENA FORETELLER and THULELAND (CHEMICAL HUNTER's at-berth data was not reported for 2024). At-berth auxiliary engine operation also produces SOx, NOx, and particulate matter — pollutants not captured in the MRV dataset but well-documented as local air quality concerns in port areas.

Inspections

Port state control is the system that keeps international shipping accountable. Under the Paris MoU, inspectors can board any foreign-flagged vessel calling at a European port and check everything from fire safety to crew working conditions. If the problems are serious enough, they can detain the ship.

STENA FORETELLER picked up 7 deficiencies at Immingham (UK) in April 2024 under an initial inspection, then 6 more at the same port in January 2025 under a more detailed inspection. Under the Paris MoU New Inspection Regime (NIR), a more detailed inspection can be mandated when overriding or unexpected factors are identified — including deficiency history, a report from a pilot or port authority, or the vessel being classified as a High Risk Ship. Having 7 deficiencies on record from the prior visit is exactly the type of factor that can trigger a more detailed inspection at the next call.

Neither inspection resulted in detention, but a recurring pattern at the same port authority is the kind of signal that compliance teams and P&I clubs track carefully. The pattern does not indicate an unsafe vessel, but it will attract closer scrutiny from inspectors and underwriters.

On the other end, CHEMICAL HUNTER has 27 inspections on record across multiple port state control regimes (Paris MoU, US Coast Guard, Mediterranean MoU, and others), with the earliest in December 2015 — all with zero deficiencies.

Practical Applications

The same data gets pulled manually every day across the industry:

• Ship brokers and charterers pull combined vessel profiles — particulars, emissions, PSC history — when fixing a vessel. This kind of consolidated due diligence typically requires querying multiple systems.
• Commodity trading desks track vessel movements to estimate cargo flows and anticipate supply disruptions. "Which tankers are in Rotterdam right now?" is a question that gets asked every morning.
• Compliance teams cross-reference inspection records against emissions data to assess counterparty risk. Chartering a vessel with a poor PSC record creates liability.
• Port operations require real-time traffic awareness for berth planning and resource allocation.
• Maritime insurers weigh inspection histories and vessel age when pricing hull and P&I coverage — CHEMICAL HUNTER's spotless record versus STENA FORETELLER's recent pattern would price very differently.

The difference is the access pattern. For ad hoc research and one-off analysis, instead of writing integration code or querying multiple systems, you describe what you need and get a synthesized report. The model decides which APIs to call, in what order, and what can run in parallel.

Try It

Everything here is reproducible. The MCP server is open source and the API has a free tier.

1. Get an API Key

Sign up at vesselapi.com and grab your API key from the dashboard.

2. Configure MCP

Use the same .mcp.json configuration from The Setup section above, replacing YOUR_API_KEY with your actual key. You'll need Node.js 18+ installed for npx.

If you prefer programmatic access without MCP, the vesselapi npm package provides a direct SDK for building your own integrations.

3. Ask Something

Start with the same prompt from The Prompt section, or try something different:

• "Find all tankers currently near Singapore and check their inspection records"
• "What vessels are in the English Channel right now? Any with recent detentions?"
• "Compare the emissions profiles of the 5 largest container ships that called at Hamburg last month"

The MCP server exposes 16 tools covering vessel search, port lookup, position tracking, emissions, inspections, casualty records, NAVTEX warnings, and area-based vessel discovery. The .mcp.json configuration works with Claude Code and similar MCP clients that read from the project root. Claude Desktop uses a separate config file in its app settings — see the MCP quickstart guide for client-specific setup.

Resources

• vesselapi-mcp on npm — The MCP server package
• VesselAPI Documentation — Full API reference
• Model Context Protocol — The MCP specification

All data pulled from VesselAPI on March 3, 2026 using the vesselapi-mcp server. Vessel positions, emissions figures, and inspection records reflect the state of the databases at the time of query. AIS-based position data is subject to the usual coverage and reporting limitations. Paris MoU risk classifications in the dashboard image (CLEAN/WATCH/MINOR) are editorial labels, not the official High Risk/Standard Risk/Low Risk (HRS/SRS/LRS) system.