# Graph Analysis Toolkit MCP for AI Agents AI Agent Connect > Graph Analysis Toolkit provides structural metrics for directed and undirected graphs. It helps you identify node importance, connectivity patterns, and structural vulnerabilities like bridges or articulation points. Use it to map out complex networks, from social connections to infrastructure topologies. ## Overview - **Category:** mathematics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_xdBP7tdhV3142pAnYxhpkay3vvS0b9yYRPI0rhvl/ai-agent-connect - **Tags:** graph-theory, network-analysis, topology, connectivity, centrality ## Description You're dealing with a web of connections and need to know which parts matter most. Maybe it's a social network where you need to find influencers, or a physical infrastructure where one failure could take down the whole system. The Graph Analysis Toolkit gives your agent the math it needs to look under the hood of these structures. Instead of guessing which nodes are important, you can get hard metrics on degree, density, and centrality. It handles the heavy lifting for complex math like Tarjan's algorithm to find strongly connected components or checking if two different structures are actually the same. It's a core part of the Vinkius marketplace, making it easy to plug into your existing workflow. You can pinpoint where a network is fragile or find isolated clusters without manual calculation. It turns abstract graph theory into actionable data for your projects. Whether you are mapping out a supply chain to find bottlenecks or analyzing a massive set of user interactions to find community hubs, this tool gives you the technical edge. It removes the need to write custom scripts to calculate these metrics from scratch. You get a clear picture of how everything connects, where the pressure points are, and how to make your network more resilient. It's built for anyone who needs to see the big picture of a complex system without getting lost in the individual threads. ## Tools ### check_graph_isomorphism Determine if two different graph structures are actually identical. This is great for verifying data consistency across sources. ### get_node_degrees Get the count of incoming and outgoing connections for any given node. It provides a quick look at local connectivity. ### analyze_graph_connectivity Assess how well a graph is interconnected and find any isolated clusters. This helps you see the overall reach of your network. ### calculate_centrality_metrics Rank nodes based on their position and influence within the network. Use this to find the most important hubs in a system. ### detect_structural_vulnerabilities Find the specific nodes or edges that would break connectivity if removed. It identifies critical bridges and articulation points. ## Prompt Examples **Prompt:** ``` Can you find the most important nodes in this social network graph? {"nodes":["A","B","C","D"],"edges":[["A","B"],["B","C"],["C","D"]],"type":"directed"} ``` **Response:** ``` Based on the graph provided, node 'B' and 'C' are the most influential. Node 'B' acts as a primary hub connecting 'A' to the rest of the network, while 'C' serves as the bridge to 'D'. Here is the centrality breakdown: * **Node B**: High centrality * **Node C**: High centrality * **Nodes A & D**: Low centrality (terminal points) ``` **Prompt:** ``` I have two different sets of data for a power grid. Are they structurally the same? Graph 1: {"nodes":["1","2","3"],"edges":[["1","2"],["2","3"]],"type":"undirected"} Graph 2: {"nodes":["X","Y","Z"],"edges":[["X","Y"],["Y","Z"]],"type":"undirected"} ``` **Response:** ``` Yes, these two graphs are isomorphic. Even though the labels are different, the underlying structure is identical: both represent a linear chain of three nodes connected by two edges. ``` **Prompt:** ``` Tell me which parts of this transit map are the most likely to cause a total shutdown if they fail. {"nodes":["Hub1","Hub2","Hub3"],"edges":[["Hub1","Hub2"],["Hub2","Hub3"]],"type":"undirected"} ``` **Response:** ``` In this transit map, **Hub2** is a critical vulnerability. It acts as an articulation point; if Hub2 fails, Hub1 and Hub3 become completely disconnected from each other. The connection between Hub1 and Hub2 also functions as a bridge. ``` ## Capabilities ### Rank node influence Get a clear picture of which points in your network hold the most weight. ### Identify network gaps Find isolated groups and see how well your data points connect. ### Spot system failures Locate critical edges and nodes that could break your entire structure. ### Compare graph shapes Check if two different datasets share the same underlying topology. ### Count connection types Get exact numbers on incoming and outgoing links for any specific node. ## Use Cases ### Infrastructure Planning A city planner wants to know which roads are critical bridges. They ask the agent to find vulnerabilities in the transit map. ### Fraud Detection A bank analyst looks for isolated clusters in transaction data. They use the tool to find groups of accounts that only interact with each other. ### Social Mapping A researcher wants to find the hubs of a community. They ask the agent to rank nodes by centrality to see who drives the conversation. ### Data Validation A developer wants to know if a simplified model of a system is structurally the same as the original. They run an isomorphism check. ## Benefits - Identify critical infrastructure risks by using detect_structural_vulnerabilities to find bridges and articulation points. - Find key influencers in social data quickly by running calculate_centrality_metrics on your node sets. - Verify data consistency across different sources by using check_graph_isomorphism to see if structures match. - Understand network health instantly with analyze_graph_connectivity to spot isolated groups or dense clusters. - Get precise connection counts for every point in your data using get_node_degrees to understand local density. ## How It Works The bottom line is you get a mathematical breakdown of your network's health and structure. 1. Provide your agent with the graph data in a standard format. 2. Ask the agent to run specific tests like centrality or vulnerability checks. 3. Receive a structured summary of the network's topology and key metrics. ## Frequently Asked Questions **What can the Graph Analysis Toolkit do for my network data?** It helps you understand the structure of your connections. You can find out which nodes are the most important, identify isolated groups, and see where your network is most fragile. **Can I use Graph Analysis Toolkit to find influencers in a social network?** Yes, that is a primary use case. It ranks nodes based on their centrality, showing you who actually drives the most interaction in a directed or undirected graph. **How does Graph Analysis Toolkit help with infrastructure planning?** It identifies bridges and articulation points in your data. This means you can see exactly which roads, power lines, or servers, if they fail, would break the rest of your system's connectivity. **Can I compare two different graph layouts with Graph Analysis Toolkit?** Yes, you can check if two graphs are isomorphic. This tells you if they have the same underlying shape, regardless of what the individual nodes are named. **Will Graph Analysis Toolkit help me find clusters in my data?** It can identify connected components and isolated groups. This is useful for finding sub-communities or finding data points that aren't reaching the rest of your network. **Is Graph Analysis Toolkit good for large datasets?** It's designed for structural analysis. It handles complex algorithms like Tarjan's to give you metrics on density and connectivity across your graph data. **How do I find the number of connections for a specific node?** Use the `get_node_degrees` tool and provide the JSON string of your graph along with an array of the node IDs you want to check. **Can I identify critical points that might break my network?** Yes, the `detect_structural_vulnerabilities` tool identifies articulation points and bridges that would disconnect parts of your graph if removed. **Is there a limit to the size of graphs I can compare for isomorphism?** The `check_graph_isomorphism` tool is designed for small graphs. Comparing very large graphs may result in a complexity error due to performance constraints.