# Shortest Path Engine MCP for AI Agents AI Agent Connect > Shortest Path Engine finds the most efficient routes through complex weighted graphs. It lets your AI client handle Dijkstra, A*, and Bellman-Ford algorithms to solve navigation, logistics, and networking problems without you having to write the underlying math. ## Overview - **Category:** algorithms - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_4jILbpr2C69ixKP1k3DHsnYznMAVua4uNkxoZ0qs/ai-agent-connect - **Tags:** dijkstra, astar, bellman-ford, graph-algorithms, shortest-path ## Description When you are building a navigation system or a game map, figuring out the best way from point A to point B is a classic headache. You don't want to spend hours debugging pathfinding logic or worrying about edge cases like negative weights or infinite loops. This Connector takes that work off your plate. It handles the heavy lifting of graph theory so your agent can just focus on the what instead of the how. You can ask it to map out a delivery route, find the quickest way through a network of servers, or calculate costs in a complex supply chain. It is built for precision, whether you are dealing with simple distances or complex weighted connections. By adding this to your toolkit via Vinkius, you give your AI the ability to reason about spatial data and network topology in real-time. It turns abstract graph data into actionable paths, making it way easier to build apps that need to move things, people, or data efficiently. ## Tools ### dijkstra Calculate the most efficient route in a graph with non-negative weights. It is the standard for most basic pathfinding tasks. ### astar Find the shortest path using A* search with spatial heuristics like Manhattan or Euclidean distance. It is best for maps with spatial data. ### bellman_ford Scan a graph to find the shortest path and detect negative cycles. Use this for networks that include negative edge weights. ## Prompt Examples **Prompt:** ``` I have a delivery network with these stops: A to B (cost 10), B to C (cost 5), A to C (cost 20). What's the cheapest way from A to C? ``` **Response:** ``` Based on the costs provided, here is the optimal route: | Path | Total Cost | | :--- | :--- | | A $\rightarrow$ B $\rightarrow$ C | 15 | | A $\rightarrow$ C | 20 | **The cheapest path is A $\rightarrow$ B $\rightarrow$ C with a total cost of 15.** ``` **Prompt:** ``` Can you check if there's a loop in my server network? Here are the connections: S to A (1), A to B (-2), B to S (1). ``` **Response:** ``` I have analyzed the network connections you provided. **Result:** A negative cycle was detected. * **Path:** S $\rightarrow$ A $\rightarrow$ B $\rightarrow$ S * **Total Weight:** 0 Because the weights sum to zero in a loop, this could cause issues in certain routing protocols. You should check these connections for configuration errors. ``` **Prompt:** ``` I'm building a small village map. If the distance between houses is Manhattan distance, what's the path from the gate to the tavern? ``` **Response:** ``` Using the Manhattan distance heuristic for your village map, here is the quickest path from the Gate to the Tavern: **Optimal Path:** [Gate, House_1, House_2, Tavern] **Total Distance:** 12 units This path avoids the main road to save travel time. ``` ## Capabilities ### Find optimal routes The agent calculates the most efficient path between nodes in a weighted graph. ### Apply spatial heuristics It uses distance estimates to speed up searches in large maps or grids. ### Identify negative cycles The tool detects infinite cost-reduction loops in complex networks. ### Map reachability It determines which parts of a network are accessible from a starting point. ### Compute total distances It provides the exact sum of weights for any calculated path. ## Use Cases ### Optimizing delivery routes A delivery company needs to find the cheapest route for a truck. The agent uses dijkstra to find the path with the lowest cost across 50 cities. ### NPC movement in RPGs An RPG needs an NPC to move across a village. The agent uses astar to calculate the path based on a custom Manhattan distance heuristic. ### Detecting network loops A sysadmin wants to find a loop in a server network. The agent uses bellman_ford to identify a negative cycle causing infinite loops. ### Infrastructure planning A city planner wants to know the best way to lay pipes. The agent maps out the reachability and total distance for a new water main. ## Benefits - Skip the math: You don't have to write Dijkstra or A* code yourself. Just describe the graph and the AI handles the rest. - Handle complex weights: Use bellman_ford to manage networks with negative costs that would break standard algorithms. - Faster spatial searches: Use astar with heuristics to find routes in game maps or city grids much faster than standard searches. - Accurate reachability: Get precise maps of which nodes are reachable, which is vital for network topology planning. - Reliable pathfinding: Ensure your agent provides consistent, mathematically correct routes every time instead of guessing the best way. ## How It Works The bottom line is your AI handles the complex math of graph traversal so you can get straight to the results. 1. Provide the AI with a list of nodes and weighted edges. 2. Tell the agent which algorithm to use based on your graph constraints. 3. Receive a clean list of the optimal path and total distance. ## Frequently Asked Questions **Can the Shortest Path Engine find the cheapest route for my delivery business?** Yes, it calculates the lowest cost path across a weighted network. You just provide the locations and costs, and it handles the math to find the most efficient route. **How does the Shortest Path Engine handle negative costs in a network?** It uses the bellman_ford algorithm specifically for this. This is great for complex financial or credit networks where costs can be negative. **Is the Shortest Path Engine good for video game NPC movement?** It is perfect for that. Using the astar tool, your AI can calculate paths across a game map using spatial heuristics like Manhattan distance. **Can I use the Shortest Path Engine to map out a city's water pipes?** Yes, it can calculate reachability maps. It shows you which areas can be reached from a main source and the total distance required for the layout. **What's the difference between the algorithms in the Shortest Path Engine?** Dijkstra is best for standard positive weights, astar is faster for maps with spatial data, and bellman_ford handles negative weights. **Does the Shortest Path Engine work for large-scale data?** It works well for any graph that fits within your AI client's context. For massive global datasets, you should feed it specific sub-networks to ensure accuracy. **When should I use the Dijkstra tool?** Use `dijkstra` when you are certain that all edge weights in your graph are zero or positive. It is highly efficient for standard shortest-path queries. **Can I use A* search without a heuristic?** The `astar` tool requires a valid `heuristicType` (either `EUCLIDEAN` or `MANSDT`) to guide its search. Without a spatial estimate, the algorithm cannot function as intended. **How does the engine handle negative edge weights?** For graphs with negative weights, you must use `bellman_ford`. This tool is specifically designed to process negative edges and identify if a negative cycle exists in your graph.