Navitia MCP Server for VS Code Copilot 11 tools — connect in under 2 minutes
GitHub Copilot in VS Code is the most widely adopted AI coding assistant, embedded directly into the world's most popular code editor. With MCP support in Agent mode, Copilot can access external data and APIs to generate context-aware code grounded in real-time information.
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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.
GitHub Copilot Agent mode brings Navitia data directly into your VS Code workflow. With a project-scoped config, the entire team shares access to 11 tools. Copilot queries live data, generates typed code, and writes tests from actual API responses, all without leaving the editor.
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 VS Code Copilot 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 VS Code Copilot via MCP
Follow these steps to integrate the Navitia MCP Server with VS Code Copilot.
Create MCP config
Create a .vscode/mcp.json file in your project root
Add the server config
Paste the JSON configuration above
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Start using Navitia
Ask Copilot: "Using Navitia, help me...". 11 tools available
Why Use VS Code Copilot with the Navitia MCP Server
GitHub Copilot for Visual Studio Code provides unique advantages when paired with Navitia through the Model Context Protocol.
VS Code is used by over 70% of developers. adding MCP tools to Copilot means your team can leverage external data without leaving their primary editor
Project-scoped MCP configs (`.vscode/mcp.json`) let you commit server configurations to your repository, ensuring the entire team shares the same tool access
Copilot's Agent mode integrates MCP tools seamlessly with file editing, terminal commands, and workspace search in a single agentic loop
GitHub's enterprise compliance and audit features extend to MCP tool usage, providing visibility into how AI interacts with external services
Navitia + VS Code Copilot Use Cases
Practical scenarios where VS Code Copilot combined with the Navitia MCP Server delivers measurable value.
Live API integration: Copilot can query an MCP server, inspect the response schema, and generate typed API client code in the same step
DevSecOps workflows: security teams can give developers access to domain intelligence tools directly in their editor for real-time vulnerability assessment during code review
Data pipeline development: Copilot fetches sample data via MCP and generates transformation scripts, validators, and test fixtures from actual API responses
Documentation generation: Copilot queries available tools and auto-generates README sections, API reference docs, and usage examples
Navitia MCP Tools for VS Code Copilot (11)
These 11 tools become available when you connect Navitia to VS Code Copilot 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 VS Code Copilot
Ready-to-use prompts you can give your VS Code Copilot 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 VS Code Copilot
Common issues when connecting Navitia to VS Code Copilot through the Vinkius, and how to resolve them.
MCP tools not available
Navitia + VS Code Copilot FAQ
Common questions about integrating Navitia MCP Server with VS Code Copilot.
Which VS Code version supports MCP?
How do I switch to Agent mode?
Can I restrict which MCP tools Copilot can access?
Does MCP work in VS Code Remote or Codespaces?
.vscode/mcp.json work in Remote SSH, WSL, and GitHub Codespaces environments. The MCP connection is established from the remote host, so ensure the server URL is accessible from that environment.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 VS Code Copilot
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.