SNCF API MCP Server

Overview

The SNCF MCP Server bridges the gap between Claude Desktop and France’s national railway network by exposing a rich set of tools that wrap the SNCF Navitia API. It transforms raw railway data into conversationally usable actions, allowing AI assistants to plan journeys, retrieve station details, and monitor disruptions without the developer having to write custom API calls. By packaging these capabilities behind a standard MCP interface, developers can integrate sophisticated train‑related functionalities into chat workflows with minimal effort.

What Problem Does It Solve?

Travelers and travel‑app developers often need real‑time access to train schedules, station amenities, and service alerts across a complex national network. Traditional approaches require handling authentication, rate limits, pagination, and data normalization manually. The SNCF MCP Server abstracts these concerns into a set of declarative tools that Claude can invoke directly. This removes boilerplate, ensures consistent error handling, and guarantees up‑to‑date information because the underlying Navitia API is queried on demand.

Core Features and Value

• Intelligent Journey Planning – Given a departure and arrival location, the server automatically resolves city or station names to precise coordinates, selects the optimal route, and returns a step‑by‑step itinerary with times and transfer details.
• Station Information Retrieval – A single tool fetches comprehensive station metadata: transport modes available, nearby points of interest, accessibility features, and geographic coordinates.
• Real‑Time Schedule Access – Users can request the next departures or arrivals for any station, receiving current timetables that reflect live delays and cancellations.
• Disruption Monitoring – The server exposes a tool to query active service disruptions, enabling proactive notifications about strikes, maintenance, or weather‑related delays.
• Smart Station Finder – Leveraging a local CSV database of European stations, the server can locate the nearest station to arbitrary coordinates or city names even when the external API fails, thanks to hard‑coded fallback coordinates for major hubs.

These features are delivered as lightweight MCP tools that can be chained in a single conversational turn, giving developers the ability to build complex travel assistants with just a few prompts.

Real‑World Use Cases

• Travel Planning Bots – A concierge chatbot can ask a user for their origin and destination, then present an itinerary with departure times, transfer points, and estimated travel duration.
• Mobile Ticketing Apps – An app can embed the MCP tools to show live departure boards, alert users of delays, and suggest alternative routes.
• Smart City Platforms – Urban mobility dashboards can query station amenities, nearby transport options, and disruption alerts to provide holistic journey recommendations.
• Accessibility Services – By retrieving station accessibility data, applications can tailor routes for passengers with mobility needs.

Integration Into AI Workflows

Developers simply register the MCP server in Claude Desktop, and the assistant automatically discovers the available tools. Each tool is described with clear input parameters and expected outputs, enabling Claude to reason about when to call a particular tool. Because the server handles authentication and data formatting internally, developers can focus on higher‑level dialogue logic rather than API plumbing. The result is a seamless, conversational experience where users receive accurate train information without leaving the chat interface.

Unique Advantages

• Modular Design – The Python wrapper is split into dedicated modules (, , , etc.), making it easy to extend or replace individual components without affecting the MCP interface.
• Robust Fallbacks – Hardcoded coordinates for major cities ensure that the journey planner remains functional even if the external API temporarily fails.
• Comprehensive Logging – Built‑in logging captures request details and errors, aiding debugging and compliance monitoring.
• Open Source Flexibility – Developers can fork the repository, tweak data sources (e.g., add local CSVs), or contribute improvements back to the community.

In sum, the SNCF MCP Server turns complex railway data into conversational actions that enrich AI assistants with real‑world travel intelligence, empowering developers to create intuitive, reliable journey planning experiences across France.