# Graph Analysis Toolkit MCP for AI Agents MCP > Graph Analysis Toolkit performs deep structural analysis on any directed or undirected network map. It calculates critical metrics like node influence, connection density, and component isolation. Use it to find bottlenecks, verify if two networks are structurally identical, or pinpoint exactly which nodes maintain the system's overall connectivity. ## Overview - **Category:** mathematics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_xdBP7tdhV3142pAnYxhpkay3vvS0b9yYRPI0rhvl/mcp - **Tags:** graph-theory, network-analysis, topology, connectivity, centrality ## Description When you need to understand how things connect—the relationships between data points, people, or systems—this MCP is your engine. It moves beyond simple lists of connections; it analyzes the structure itself. For instance, if you’re mapping a supply chain, this toolkit doesn't just tell you which facilities are connected; it tells you *how* connected they are and where the single point of failure lies. You can find key nodes that act as central hubs or identify smaller groups within a massive network that aren't talking to anyone else. It even checks if two completely different networks, like an old infrastructure map and a new one, share the exact same underlying structure. If you use Vinkius, you connect this MCP alongside dozens of others, giving your AI agent total access to advanced topological analysis for any industry. ## Tools ### analyze_graph_connectivity Assesses how interconnected a graph is and identifies any completely isolated groups or components within the network map. ### calculate_centrality_metrics Ranks every node in the graph based on its calculated influence, showing which entities are most important to the system. ### check_graph_isomorphism Determines mathematically if two separate graphs share the exact same structure, even if they use different labels or nodes. ### detect_structural_vulnerabilities Flags specific edges and nodes that are critical to maintaining network connectivity; removing them causes failure. ### get_node_degrees Provides a direct count of the connections for each node, tracking both incoming and outgoing relationships. ## Prompt Examples **Prompt:** ``` Hey, check out this network diagram. Which nodes are acting as bottlenecks, and why? ``` **Response:** ``` **Structural Vulnerability Report** The following edges/nodes represent critical single points of failure: * `Edge (A -> C)`: If this fails, the connection between A and C is severed. * `Node B`: This node is a bridge. Losing it disconnects Cluster 1 from Cluster 2. **Recommendation:** Focus redundancy efforts on Node B. ``` **Prompt:** ``` I have two maps of our network, one old and one new. Are they actually the same structure? ``` **Response:** ``` **Isomorphism Check Result:** ✅ **Result: ISOMORPHIC** The underlying graph structures are identical. The pattern of connections is preserved, even though the nodes and edges have been relabeled. *Similarity Score:* 100% ``` **Prompt:** ``` In this dataset showing user interactions, who has the highest influence score? ``` **Response:** ``` **Top 3 Centrality Rankings** The most influential nodes are: 1. **User X**: (Centrality Score: 0.89) Acts as the primary hub connecting departments. 2. **Admin Portal**: (Centrality Score: 0.75) Connects all major subsystems. 3. **API Gateway**: (Centrality Score: 0.61) ``` ## Capabilities ### Determine node connection counts Get precise figures for how many incoming and outgoing connections each node has within the graph. ### Assess overall network connectedness Measure the degree of interconnectedness across the entire map, helping you identify completely isolated groups or components. ### Rank nodes by influence and position Calculate complex metrics that show which nodes are most central or influential to the overall network structure. ### Identify critical structural vulnerabilities Pinpoint specific nodes or edges whose removal would drastically break the graph's connectivity. ### Compare two graph structures for identity Check mathematically if two separate graphs, regardless of how they are labeled, have the exact same structural pattern. ## Use Cases ### Mapping Infrastructure Dependencies A utility company needs to know if a single substation failure will cause widespread outages across multiple grids. They ask their agent to run structural vulnerability detection, immediately identifying the primary feeder lines that must be prioritized for redundancy. ### Analyzing Social Network Influence A marketing team wants to find the most influential users in a community chat dataset. The agent runs centrality metrics on the connection graph, delivering a ranked list of nodes whose activity drives the most engagement. ### Comparing Legacy and Modern Systems An IT department receives two network diagrams—one old, one new. They use isomorphism checking to determine if the underlying structure is identical, saving months of manual comparison work. ### Identifying Data Clusters in Research A researcher maps out how genes interact with different proteins. The agent uses connectivity analysis to separate distinct, isolated functional groups that were previously hidden within the large dataset. ## Benefits - Pinpoint Single Points of Failure: Use `detect_structural_vulnerabilities` to instantly find which critical nodes or edges, if compromised, will break your entire system. - Understand Influence: `calculate_centrality_metrics` gives you a score for every node, telling you precisely who or what holds the most weight in the network. - Verify Structural Integrity: Need to know if two systems are fundamentally the same? `check_graph_isomorphism` provides a definitive structural comparison. - Map Connections Fast: Quickly get connection counts and assess overall interconnectedness using tools like `get_node_degrees` and `analyze_graph_connectivity`. - Identify Hidden Groups: Figure out isolated components that might be overlooked. This MCP helps you map every corner of your network, no matter how disconnected it seems. ## How It Works The bottom line is, you give it a network map, and it gives you the mathematical proof of its structural health and weaknesses. 1. You provide your AI client with a graph structure—a list of nodes and the edges connecting them. 2. Your agent uses this MCP to run specific analyses, selecting tools like `calculate_centrality_metrics` or `detect_structural_vulnerabilities` based on your question. 3. The tool returns detailed metrics: connection counts, influence rankings, or confirmation that two graphs are isomorphic. ## Frequently Asked Questions **How does the Graph Analysis Toolkit find single points of failure in a system?** It pinpoints specific nodes or connections that, if they fail or are removed, would break the overall network into smaller, non-connected pieces. This helps you focus on building redundancy where it matters most. **Can this MCP tell me which part of a business process is most critical?** Yes. By modeling processes as nodes and interactions as edges, the toolkit calculates centrality metrics to give you an objective score showing which parts of your operation are most influential. **Do I need this MCP if I just want to count connections?** While it can list counts using `get_node_degrees`, the real power is in understanding *why* those connections matter—which nodes are crucial versus which ones are simply numerous. **Is there a way for Graph Analysis Toolkit to compare two different network diagrams?** Absolutely. The toolkit includes an isomorphism check that mathematically proves if two graphs share the same fundamental structure, even if they use completely different labels or nodes.