Navitia MCP Server for Cursor 11 tools — connect in under 2 minutes
Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.
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{
"mcpServers": {
"navitia": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
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About Navitia MCP Server
Connect your Navitia multimodal transit API to any AI agent and take full control of European public transportation planning, real-time service monitoring, and accessibility analysis through natural conversation.
Cursor's Agent mode turns Navitia into an in-editor superpower. Ask Cursor to generate code using live data from Navitia and it fetches, processes, and writes. all in a single agentic loop. 11 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.
What you can do
- Multimodal Journey Planning — Plan door-to-door trips combining metro, bus, tram, RER, regional rail, walking, cycling, bike-sharing, and car
- Place Search — Find transit stops, stations, addresses, and POIs with autocomplete search across French and European networks
- Real-Time Departures — Check upcoming departures at any transit stop with ETAs, platforms, and delay indicators
- Arrival Tracking — Monitor incoming services for passenger pickup and connection coordination
- Stop Schedules — Access complete timetables for any transit stop with weekday/weekend/holiday patterns
- Nearby Discovery — Find all transit stops near any geographic coordinate with distance calculations
- Service Disruptions — Check active alerts, strikes, maintenance works, and operational notices across networks
- Line Exploration — Browse all transit lines by mode type (metro, bus, tram, rail) with operator affiliations
- Network Analysis — Research transit operators including RATP, SNCF, TCL, RTM, and regional authorities
- Isochrone Mapping — Generate accessibility maps showing reachable areas within time limits from any point
- Coverage Discovery — List all available coverage regions with data validity periods and contributor information
The Navitia MCP Server exposes 11 tools through the Vinkius. Connect it to Cursor in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Navitia to Cursor via MCP
Follow these steps to integrate the Navitia MCP Server with Cursor.
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
Save the file
Cursor will automatically detect the new MCP server
Start using Navitia
Open Agent mode in chat and ask: "Using Navitia, help me...". 11 tools available
Why Use Cursor with the Navitia MCP Server
Cursor AI Code Editor provides unique advantages when paired with Navitia through the Model Context Protocol.
Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context
Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards
MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment
VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools
Navitia + Cursor Use Cases
Practical scenarios where Cursor combined with the Navitia MCP Server delivers measurable value.
Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP
Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically
Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates
Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data
Navitia MCP Tools for Cursor (11)
These 11 tools become available when you connect Navitia to Cursor via MCP:
get_arrivals
Returns list of arriving services with line names and codes, origins, scheduled and real-time arrival times, platform information, delay indicators, and mode types. Essential for passenger pickup coordination, arrival monitoring, connection planning, and real-time arrival boards. AI agents use this when users ask "when does the next train arrive at this station", "show incoming services at stop X", or need to track arriving services for passenger coordination. Supports both theoretical schedules and real-time arrival predictions when operator data feeds are available. Get upcoming arrivals at a specific transit stop
get_coverage
Shows which cities and metropolitan areas are covered, data freshness indicators, and the contributing transit authorities for each region. Essential for discovering which transit networks are accessible through the API, validating region IDs for subsequent queries, understanding data coverage scope, and planning integration scope. AI agents should use this when users ask "what cities does Navitia cover", "show me all available transit regions", or need to identify the correct region ID (e.g., "fr-idf" for Paris/Ile-de-France) before making region-specific queries for lines, disruptions, or journeys. List all available coverage regions in the Navitia platform
get_departures
Returns list of departing services with line names and codes, destinations, scheduled and real-time departure times, platform or bay information, delay indicators, direction codes, and physical/commercial mode types (metro, bus, tram, RER, Transilien). Supports real-time data when available from operators. Essential for passenger information displays, departure boards, real-time transit monitoring, and journey planning. AI agents should reference this when users ask "when is the next metro from this station", "show departures from stop ID X", or need to monitor upcoming services at a known transit stop. Use data_freshness parameter to choose base_schedule (theoretical timetable) or realtime (including disruptions and delays). Get upcoming departures from a specific transit stop
get_disruptions
Returns active disruptions with affected lines, routes, stops, and networks, disruption descriptions, severity levels (minor, major, blocking), start and end timestamps, cause types (incident, maintenance, strike, weather), impact descriptions, and detour or alternative service recommendations. Covers all modes including metro, bus, tram, RER, Transilien, and regional rail across French and European networks. Essential for disruption awareness, passenger communication, journey reliability monitoring, and travel planning during service changes. AI agents should reference this when users ask "are there any disruptions on the Paris metro", "is there a strike on SNCF trains", or need to check service reliability before planning journeys. Get active service disruptions and alerts for a transit region
get_isochrone
Returns GeoJSON polygon boundaries, reachable area statistics, travel time bands, and accessibility metrics. Essential for urban planning, real estate location analysis, accessibility studies, job market research, school catchment analysis, and understanding transit connectivity. AI agents use this when users ask "what area can I reach within 30 minutes by metro from this address", "show me the accessible zone in 45 minutes by public transport", or need to analyze geographic accessibility from a specific location for housing, employment, or service planning. Generate an isochrone map showing reachable area from a point within a time limit
get_lines
Returns lines with codes, names, network affiliations, physical modes (metro, bus, tram, RER, rail), commercial modes, colors, text colors, route counts, and operational information. Covers metro systems (RATP Paris, TCL Lyon, TCL Marseille), bus networks, tramway systems, RER lines, Transilien suburban rail, and regional TER services across France. Essential for transit network exploration, line identification, route planning context, network analysis, and understanding service coverage by mode type. AI agents should use this when users ask "list all metro lines in Paris", "show me all tram lines in Lyon", or need line metadata to understand transit network structure and operator affiliations. List all transit lines in a coverage region
get_nearby_stops
Returns nearby objects sorted by distance with coordinates, names, types (stop point, stop area, station, address, POI), distances from search point, served lines, and administrative information. Essential for location-based transit discovery, "stops near me" features, geographic transit analysis, multimodal connection identification, and traveler navigation. AI agents use this when users ask "what metro stations are near my current location", "find transit stops within 500m of these coordinates", or need to discover accessible transit options from a specific geographic point. Supports filtering by object type (stop_point, stop_area, poi, address) and adjustable search radius. Find transit stops near a geographic coordinate
get_networks
Returns network information including names, codes, contributing authorities, coverage areas, associated lines and routes, and operational status. Covers major operators like RATP (Paris metro/bus/tram), SNCF (RER/Transilien/TER), TCL (Lyon), RTM (Marseille), TCL (Toulouse), and dozens of regional and local operators across France. Essential for operator research, network scoping, regional transit analysis, and understanding service governance structure. AI agents should reference this when users ask "what operators run transit in Paris", "list all networks in Ile-de-France", or need to identify transit operators for a specific region before querying lines or disruptions. List all transit operators and networks in a coverage region
get_stop_schedule
Returns all scheduled departures with routes, destinations, first and last departure times, service frequency, headway signatures (days of operation), and physical/commercial mode information. Shows complete timetable structure including weekday, weekend, and holiday service patterns. Essential for comprehensive schedule analysis, journey planning at specific times, timetable visualization, and understanding service frequency throughout the day. AI agents should use this when users ask "show me the full timetable for this metro station", "what times does this bus run on Sundays", or need complete schedule data for a transit stop. Supports depth parameter to control level of detail in route and destination information. Get full timetable for a specific transit stop
plan_journey
Supports combining public transit (metro, bus, tram, regional trains, high-speed rail), walking, cycling, car, bike-sharing (Vélib), and ridesharing. Returns complete itineraries with departure and arrival times, total duration, number of transfers, detailed legs with mode types, line names, operators, intermediate stops, walking distances, real-time disruption alerts, accessibility information (wheelchair access), and fare estimates. Essential for travel planning, multimodal route comparison, passenger information systems, and Mobility-as-a-Service applications across France and European cities. AI agents should use this when users ask "how do I get from Gare du Nord to Eiffel Tower", "plan a trip from Lyon Part-Dieu to Marseille", or need multimodal journey options with timing, transfers, and accessibility details. Supports traveler profiles including wheelchair, slow walker, fast walker, and luggage. Plan a multimodal trip between two locations in France or Europe
search_places
Returns transit stops (stop areas, stop points), stations (metro, tram, bus, rail), addresses, administrative areas, and points of interest with their IDs, names, coordinates, types, and administrative information. Supports autocomplete-style search for journey planning interfaces and location discovery. Essential for stop discovery, address resolution, geocoding, journey origin/destination identification, and building location-based transit features. AI agents should use this when users ask "find the metro station near Champs-Elysees", "search for stops called Republique", or need to identify place IDs and coordinates for use in journey planning queries. Results include embedded links to departures, schedules, and nearby objects for further exploration. Search for transit stops, stations, addresses, and POIs by name
Example Prompts for Navitia in Cursor
Ready-to-use prompts you can give your Cursor agent to start working with Navitia immediately.
"Plan a trip from Gare du Nord to the Eiffel Tower using public transit in Paris."
"Show me all metro departures from Chatelet station in the next 20 minutes."
"What areas can I reach within 45 minutes by public transit from Lyon Part-Dieu station?"
Troubleshooting Navitia MCP Server with Cursor
Common issues when connecting Navitia to Cursor through the Vinkius, and how to resolve them.
Tools not appearing in Cursor
Server shows as disconnected
Navitia + Cursor FAQ
Common questions about integrating Navitia MCP Server with Cursor.
What is Agent mode and why does it matter for MCP?
Where does Cursor store MCP configuration?
mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.Can Cursor use MCP tools in inline edits?
How do I verify MCP tools are loaded?
Connect Navitia with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Navitia to Cursor
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.