GraphHopper MCP. Optimize routes, solve logistics, and map data.
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GraphHopper. Calculate optimal routes, solve complex logistics, and analyze geospatial data directly from your AI client. This server handles everything from calculating drive times between multiple stops to defining precise service areas (isochrones) and validating raw GPS tracks against real street data.
It provides full control over routing, geocoding, and fleet optimization through natural conversation, making it a core utility for logistics and GIS professionals.
What your AI agents can do
Calculate distance isochrone
Generates a JSON payload defining the physical boundary (isochrone) of a location.
Calculate heavy route
Identifies precise active arrays for calculating multi-stop routes.
Calculate reachability polygon
Creates a structured polygon defining the area reachable within a specific time limit.
Generates a JSON payload defining the physical boundary (isochrone) that can be reached within a specified time limit.
Identifies precise driving paths that connect multiple geographical points in an optimal sequence.
Generates a structured matrix containing travel times and distances between a set of multiple points.
Translates plain text street addresses into precise Latitude/Longitude coordinates for mapping.
Optimizes routes for multiple vehicles, ensuring constraints like time windows and vehicle capacity are met.
Cleans up raw GPS data (GPX files) by snapping every point perfectly onto known road networks.
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GraphHopper MCP Server: 10 Tools for Routing & Geospatial Data
Use these ten tools to calculate routes, define service areas, clean GPS data, and solve complex logistics problems directly within your AI workflow.
019d75a4calculate distance isochrone
Generates a JSON payload defining the physical boundary (isochrone) of a location.
019d75a4calculate heavy route
Identifies precise active arrays for calculating multi-stop routes.
019d75a4calculate reachability polygon
Creates a structured polygon defining the area reachable within a specific time limit.
019d75a4calculate routing matrix
Inspects and generates deep internal arrays containing travel times and distances between multiple points.
019d75a4calculate url route
Retrieves explicit, lightweight step-by-step driving directions between two or more points.
019d75a4poll vrp solution
Checks the status and retrieves the final optimized solution for a complex multi-vehicle routing problem.
019d75a4reverse geocode
Matches GPS coordinates to a physical street address by extracting properties from OSM bindings.
019d75a4search geocode
Finds specific, bounded routing areas within the Headless GraphHopper Engine using coordinates.
019d75a4snap gpx to road
Cleans raw GPS tracks (GPX) by snapping every coordinate point perfectly onto known street vectors.
019d75a4submit vrp optimizer
Dispatches a request to solve complex, automated vehicle routing problems.
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What you can do with this MCP connector
GraphHopper handles everything from figuring out the best driving path to cleaning up messy GPS data. You'll get full control over routing, geocoding, and fleet management right through your AI client. You're dealing with complex location data—everything from simple point-to-point trips to optimizing whole fleets.
Calculating Service Boundaries
When you need to know the exact area a driver can reach in a set amount of time, you can use calculate_distance_isochrone to generate a JSON boundary (isochrone). Similarly, calculate_reachability_polygon creates a structured polygon defining the area you can get to within a specific time limit.
Planning Routes and Connections
To figure out the fastest way through multiple stops, you can call calculate_heavy_route to identify precise active arrays for multi-stop routes. If you just need simple, step-by-step driving directions between two or more places, calculate_url_route pulls those explicit, lightweight routes. For complex logistics involving multiple vehicles, you first submit the problem using submit_vrp_optimizer and then check the results with poll_vrp_solution to get the final optimized plan.
Analyzing Location Data
If you need to know the travel times and distances between a bunch of points, calculate_routing_matrix generates deep internal arrays for analysis. For finding specific areas, search_geocode looks for bounded routing zones within the Headless GraphHopper Engine using coordinates. You can also turn plain text street addresses into precise Lat/Long coordinates using search_geocode and reverse_geocode, which matches GPS coordinates back to a physical street address by pulling properties from OSM bindings.
Raw GPS tracks need cleaning? Run snap_gpx_to_road to snap every point in a raw GPX track perfectly onto known street vectors.
Handling Complex Logistics
To solve massive fleet problems, submit_vrp_optimizer takes your JSON targets and optimizes routes while checking constraints like time windows and vehicle capacity.
How GraphHopper MCP Works
- 1 Subscribe to the GraphHopper server and input your unique API Key.
- 2 Ask your AI client to perform a specific geospatial task (e.g., 'Calculate the 15-minute reachability polygon from downtown').
- 3 The server executes the required tool (like
calculate_reachability_polygon) and returns structured data (like coordinates or routes) directly to your agent.
The bottom line is, you tell your agent what location problem you have, and it runs the necessary calculation without you needing to open a dashboard or manually handle API calls.
Who Is GraphHopper MCP For?
This is for logistics managers, GIS analysts, and supply chain planners. If your job involves knowing how far a delivery truck can get, or mapping out the most efficient sequence for 20 stops, this tool saves you days of spreadsheet work and manual API calls. It turns complex spatial math into a simple conversation.
Optimizes multi-stop delivery routes and runs Vehicle Routing Problem (VRP) simulations to cut fuel costs and hit deadlines.
Performs geocoding lookups and analyzes isochrone reachability to determine optimal site locations for new branches or service areas.
Calculates large routing matrices and validates distance tables across entire regional distribution networks to model capacity.
Tests and debugs complex routing parameters or map matching logic directly from the chat interface instead of writing boilerplate API client code.
What Changes When You Connect
- Solve complex fleet logistics instantly. Use
submit_vrp_optimizerandpoll_vrp_solutionto check time windows and vehicle capacity constraints without writing complex JSON payloads. - Define service zones with precision.
calculate_reachability_polygonlets you map out exactly what area is covered in 10 minutes, ideal for site selection. - Stop dealing with raw, messy GPS data.
snap_gpx_to_roadvalidates and cleans raw GPX tracks by snapping every point to the actual street geometry. - Get precise location data from text. Use
search_geocodeorreverse_geocodeto turn 'Main Street, Paris' into usable coordinates for any database. - Analyze entire grids at once.
calculate_routing_matrixgenerates massive tables of travel times and distances, letting you compare multiple points simultaneously. - Get detailed turn-by-turn directions.
calculate_url_routeretrieves lightweight, step-by-step driving directions for simple point-to-point trips.
Real-World Use Cases
Redefining service boundaries
A regional manager needs to know if a potential new office location is reachable within a 15-minute drive from three major hubs. Instead of manually drawing zones, they ask their agent to run calculate_reachability_polygon. The agent returns the exact boundary coordinates, letting the manager confirm if the site fits the service criteria.
Debugging messy field reports
A developer receives a raw GPX file from a field team that has GPS jumps and errors. Instead of spending hours cleaning the data, they ask the agent to run snap_gpx_to_road. The agent cleans the track instantly, making the data usable for immediate analysis.
Optimizing a multi-stop delivery route
A logistics planner has 20 stops and multiple vehicles, each with capacity limits and time windows. They ask the agent to submit_vrp_optimizer. The server runs the complex math, and the planner uses poll_vrp_solution to get the final, optimized sequence of stops.
Analyzing network coverage
A data analyst needs to compare the travel time between five different distribution centers to every other center. They ask the agent to run calculate_routing_matrix. The server returns a clean grid of all travel times and distances, allowing immediate comparison.
The Tradeoffs
Assuming simple point-to-point routing is enough
A user thinks, 'I just need a route from A to B.' They use a simple URL-based method and miss the total travel time across multiple legs.
→
For simple routes, use calculate_url_route. If you have multiple stops or need to compare many paths, use calculate_heavy_route or calculate_routing_matrix to get the complete, structured data.
Handling raw GPS data without cleaning it
A user pastes a raw GPX track into a basic mapping tool, and the resulting line is jagged or jumps across roads, making it useless for accurate distance calculations.
→
Always run the data through snap_gpx_to_road. This tool fixes the messy data by snapping every point perfectly to the street network, ensuring your measurements are accurate.
Trying to solve logistics without constraints
A planner tries to optimize 5 vehicles but forgets to specify that Vehicle 1 only works 8 hours a day or that Vehicle 2 can only carry 100 units.
→
You must use the VRP tools. First, use submit_vrp_optimizer to define all constraints (time windows, capacity). Then, use poll_vrp_solution to retrieve the mathematically verified, optimized route.
When It Fits, When It Doesn't
Use this server if your problem is purely geospatial: finding paths, calculating distances, or defining physical areas on a map. You need it when you're dealing with coordinates, addresses, or raw GPS files. Don't use it if your problem is about inventory management, employee scheduling, or sales funnels—that's a database or CRM tool. If you only need to convert a single address to coordinates, search_geocode is sufficient. But if you need to verify that address and know the travel time from a second point, you need the full suite of tools like calculate_routing_matrix or reverse_geocode to tie the data together. This is a powerful math engine, not a simple search bar.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GraphHopper. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Mapping a service area shouldn't involve drawing dozens of overlapping circles.
Before this, determining a service boundary was a tedious, multi-step process. You'd start with a central point, then manually calculate the radius for a given time limit. You'd have to overlay multiple circles on a map, adjusting the center point every time you wanted to check a different corner of the zone. It was a lot of guesswork and visual approximation.
Now, you simply ask your agent to calculate the polygon. The server runs the geometry and returns the precise, closed coordinates defining the boundary. You get a mathematically verified polygon, ready to be fed directly into a database or spreadsheet.
GraphHopper MCP Server: Get verified routes and coordinates from chat.
You no longer need to open the GraphHopper website, manually enter API keys, and navigate through different dashboards just to get a basic route. You don't have to copy-paste coordinates into a separate spreadsheet to calculate a routing matrix.
Your AI client handles the API calls and the complex data parsing. You just ask the question, and you get the structured, ready-to-use data back. It's immediate, structured, and actionable.
Common Questions About GraphHopper MCP
How do I use GraphHopper MCP Server for simple point-to-point driving directions? +
Use calculate_url_route. This tool provides the optimal driving path and step-by-step directions between two or more specific GPS coordinates.
Can GraphHopper MCP Server solve complex delivery routes? +
Yes, use the Vehicle Routing Problem (VRP) tools. Start by calling submit_vrp_optimizer with all constraints, then use poll_vrp_solution to get the final, optimized sequence.
What is the best way to clean up messy GPS tracking data using GraphHopper MCP Server? +
Use snap_gpx_to_road. This tool processes raw GPX files and corrects imprecise GPS jumps by snapping every coordinate point perfectly onto the known street network.
How do I find the address of a set of coordinates with GraphHopper MCP Server? +
Run reverse_geocode. This tool performs structural extraction, matching the given GPS pins exactly against named physical streets to give you the full address.
Can I compare travel times between many points with GraphHopper MCP Server? +
Yes, run calculate_routing_matrix. This generates a structured array that contains travel times and distances between every point in your defined grid.
How does the `calculate_url_route` tool work for finding simple directions using GraphHopper MCP Server? +
It retrieves explicit, lightweight directions by tracing cloud logs. This function is ideal for quick, simple routes where you just need the basic turn-by-turn steps without complex optimization or matrix calculations.
What should I use if I need to check if a location is reachable within a specific time using `calculate_reachability_polygon` with GraphHopper MCP Server? +
The calculate_reachability_polygon tool generates a precise polygon defining the area you can reach within a set time limit. Use this when site selection or defining delivery zones requires knowing the boundary, not just a single path.
How do I handle multiple vehicles and time constraints using `submit_vrp_optimizer` with GraphHopper MCP Server? +
The submit_vrp_optimizer tool runs automated checks for complex vehicle routing. You can input multiple vehicles, time windows, and capacity constraints to solve full-scale logistics problems that require sequencing.
Can my agent calculate reachability zones using time limits? +
Yes. Use the 'calculate_reachability_polygon' tool. Provide a starting point and a time limit in seconds. The agent will retrieve the isochrone polygon defining exactly what area is reachable within that duration natively.
How do I optimize a multi-stop delivery route via chat? +
Use the 'submit_vrp_optimizer' tool. Provide a JSON payload defining your vehicles and service stops. The agent will trigger the VRP solver to calculate the most efficient sequence, accounting for time windows and capacities synchronously.
Can I perform reverse geocoding to find a street name through the agent? +
Absolutely. Use the 'reverse_geocode' tool. Provide the latitude and longitude. Your agent will analyze the global OSM bounds to match the coordinates exactly against the nearest physical street address flawlessy.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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