# GraphHopper MCP for AI Agents AI Agent Connect > GraphHopper MCP for AI agents. This tool handles complex logistics, from calculating multi-stop delivery routes to generating reachability polygons. It turns raw coordinates and addresses into actionable fleet plans and geospatial data without you having to touch a spreadsheet or a complex API dashboard. ## Overview - **Category:** developer-tools - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_cQUPJxjf4FYe5KOaZuMFakr1XD1twCbw4cSXR3BG/ai-agent-connect - **Tags:** routing, pathfinding, openstreetmap, fleet-optimization, geospatial-data, navigation ## Description GraphHopper lets you take full control of your geospatial routing, geocoding, and fleet optimization through natural conversation. When you connect this to your agent, you stop wrestling with manual routing tables and static maps. Instead of copy-pasting addresses into a web tool, you can just tell your agent to find the best way to hit ten different stops in a specific city. It handles the heavy lifting of calculating travel times and distances across real road networks. If you need to know where a delivery driver can actually get to within 30 minutes, your agent can draw that boundary for you. It even solves the puzzle of how to assign multiple stops to a fleet of vehicles while respecting time windows and truck capacities. You get to see the results right in your chat window, making it way easier to adjust plans on the fly. Because Vinkius makes it so simple to manage these connections, you can swap between different geospatial tasks without jumping between tabs. It's about moving from 'how do I calculate this' to 'where should we send the trucks today.' You can also use it to clean up messy GPS data, turning jittery tracks into clean paths that follow the actual streets. Whether you're trying to figure out the best spot for a new warehouse or just need to verify that a specific coordinate actually hits a real street, this connection does the work for you. ## Tools ### calculate_routing_matrix Create a table of travel times and distances between many different points. This is perfect for complex grid logistics. ### reverse_geocode Turn GPS coordinates into actual street names and physical locations. This helps you verify where a specific pin sits on a map. ### calculate_url_route Get lightweight directions for simple point to point paths. It's great for quick checks on travel time and distance. ### calculate_heavy_route Plan complex routes with many stops and specific vehicle constraints. It handles the hard math of multi-stop geometries. ### submit_vrp_optimizer Send a request to solve a vehicle routing problem with specific constraints. It figures out the most efficient way to use your fleet. ### poll_vrp_solution Check the status and get the final result of a complex fleet optimization task. Use this to see the completed delivery plan. ### search_geocode Convert human addresses into precise latitude and longitude coordinates. This is the starting point for any spatial analysis. ### calculate_distance_isochrone Generate a polygon showing how far you can travel in a set amount of time. Use this to see your actual delivery range. ### calculate_reachability_polygon Define specific rules to export reachability areas. It helps set boundaries for service zones. ### snap_gpx_to_road Clean up messy GPS tracks by snapping them onto actual street vectors. It makes raw data look like a real path. ## Prompt Examples **Prompt:** ``` I need to plan a route for a delivery van that has to hit 8 different addresses in downtown Chicago today. What's the best order? ``` **Response:** ``` I've calculated the best route for your 8 stops in downtown Chicago. Here is the optimized order to minimize travel time: **Optimized Route:** 1. 123 Main St 2. 456 Oak Ave 3. 789 Pine Rd 4. 101 Birch Ln 5. 202 Cedar Ct 6. 303 Elm St 7. 404 Maple Dr 8. 505 Willow Ln **Summary:** - **Total Distance:** 12.4 miles - **Estimated Time:** 45 minutes - **Key Notes:** Includes one-way street adjustments and heavy vehicle restrictions for downtown. ``` **Prompt:** ``` Show me a map of where people can drive to within 15 minutes from this zip code: 60601. ``` **Response:** ``` I've mapped out the 15-minute reachability zone for zip code 60601. **Reachability Summary:** - **Primary Zone:** Covers the heart of downtown Chicago. - **Travel Time:** 15 minutes (approximate). - **Boundaries:** The zone extends primarily North and West along major corridors. Would you like the specific polygon coordinates or a list of addresses that fall within this boundary? ``` **Prompt:** ``` I have a list of 5 locations. Give me a table showing the travel time and distance between every single one of them. ``` **Response:** ``` Here is the travel time and distance matrix for your 5 locations: | From \ To | Loc A | Loc B | Loc C | Loc D | Loc E | | :--- | :--- | :--- | :--- | :--- | :--- | | **Loc A** | 0 min | 8 min | 14 min | 22 min | 30 min | | **Loc B** | 8 min | 0 min | 12 min | 19 min | 28 min | | **Loc C** | 14 min | 12 min | 0 min | 15 min | 25 min | | **Loc D** | 22 min | 19 min | 15 min | 0 min | 10 min | | **Loc E** | 30 min | 28 min | 25 min | 10 min | 0 min | ``` ## Capabilities ### Plan multi-stop delivery routes Your agent calculates the fastest path for several stops while accounting for road constraints. ### Map reachability zones Generate a polygon showing exactly how far a vehicle can travel in a specific timeframe. ### Solve vehicle routing problems Automatically assign multiple deliveries to a fleet based on capacity and time windows. ### Convert addresses to coordinates Turn human-readable street addresses into precise latitude and longitude points. ### Clean up GPS tracks Snap raw GPS data onto actual street vectors to remove imprecise jumps and errors. ### Generate distance matrices Create a full grid of travel times and distances between many different locations at once. ## Use Cases ### Multi-stop delivery planning A manager asks the agent to plan a route for a van hitting 8 different addresses. The agent uses `calculate_heavy_route` to find the fastest path. ### Retail site selection An analyst wants to see where people can drive in 15 minutes from a new store. The agent uses `calculate_distance_isochrone` to show the area. ### Fleet load balancing A dispatcher needs to assign 50 packages to 4 trucks. The agent uses `submit_vrp_optimizer` to balance the load across the fleet. ### Cleaning messy GPS logs A developer has a messy GPX file from a bike path. The agent uses `snap_gpx_to_road` to make it usable on a map. ## Benefits - Stop manual spreadsheet math by using `calculate_routing_matrix` to get distance tables instantly. This saves hours of manual data entry for large-scale logistics planning. - Define clear delivery zones with `calculate_reachability_polygon` to see exactly where your drivers can go. You can use this to set realistic service boundaries for your customers. - Fix jumpy GPS data by using `snap_gpx_to_road` to align tracks with real streets. It turns messy raw data into clean, usable paths for your maps and reports. - Solve multi-vehicle logistics by using `submit_vrp_optimizer` to handle capacity and time windows. This ensures your fleet is used efficiently without overbooking drivers. - Turn addresses into map data quickly with `search_geocode` for faster site selection. You can check multiple locations at once to find the best spot for a new branch. - Get precise turn-by-turn directions with `calculate_url_route` for simple navigation checks. It's a fast way to get travel times and distances for quick planning. ## How It Works The bottom line is you get to trade manual logistics calculations for instant, conversation-based routing and fleet planning. 1. Get your GraphHopper API Key from your dashboard. 2. Enter that key into your Vinkius connection settings. 3. Ask your agent to plan a route or map a delivery zone. ## Frequently Asked Questions **How can I use the GraphHopper MCP to plan multi-stop delivery routes?** You can ask your agent to calculate the best path for several stops at once. It handles the complex routing math, including road constraints, and gives you a clear sequence for your drivers. **Can the GraphHopper MCP help me find the best location for a new warehouse?** Yes, it can help you visualize reachability zones. By generating polygons based on travel time, you can see exactly which areas are accessible from a potential site. **Does the GraphHopper MCP support vehicle routing with time windows?** Yes, it can solve vehicle routing problems by factoring in specific constraints like truck capacity and delivery time windows to optimize your fleet. **How do I get reachability polygons using the GraphHopper MCP?** Just ask your agent to show you the reachability for a specific area. It will generate a polygon based on the real road network and your desired time limit. **Can I use the GraphHopper MCP to clean up messy GPS tracks?** Yes, it can snap raw GPS data onto actual streets. This removes the 'jitter' from raw coordinates and makes the data much easier to use on a map. **Will the GraphHopper MCP help me calculate distance matrices for my fleet?** It can generate a full table of travel times and distances between multiple locations, which is perfect for analyzing distribution networks and logistics costs. **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.