MCP For Claude Marketplace
Graph Analysis Toolkit

Graph Analysis Toolkit MCP Connector for Claude

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Deep structural analysis of directed and undirected graphs, providing metrics on connectivity, topology, and node importance.

5 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

The Graph Analysis Toolkit is a specialized engine for deep structural analysis of directed and undirected graphs. It provides essential metrics including degree (in-degree and out-degree), density, connected components, and strongly connected components using Tarjan's algorithm. You can also identify structural vulnerabilities like articulation points and bridges, calculate centrality measures such as degree and betweenness centrality approximation, and perform isomorphism checks for small graphs. Use get_node_degrees to find connection counts, analyze_graph_connectivity for connectivity metrics, detect_structural_vulnerabilities for critical nodes/edges, calculate_centrality_metrics for node importance, and check_graph_isomorphism to compare graph structures.

graph-theorynetwork-analysistopologyconnectivitycentrality

5 tools expose this connector's capabilities to your AI agent.

detect_structural_vulnerabilities

Identify critical nodes and edges that break connectivity

calculate_centrality_metrics

Rank nodes based on influence and position

check_graph_isomorphism

Determine if two graphs are structurally identical

get_node_degrees

Determine incoming and outgoing connections for nodes

analyze_graph_connectivity

Assess graph interconnectedness and identify isolated groups

See how to talk to your AI agent using Graph Analysis Toolkit.

What is the degree of node 'A' in this graph: {"nodes":["A","B"],"edges":[["A","B"]],"type":"undirected"}?

Node 'A' has an in-degree of 1 and an out-degree of 1.

Check if these two graphs are structurally identical: Graph 1: {"nodes":["1","2"],"edges":[["1","2"]],"type":"undirected"} and Graph 2: {"nodes":["A","B"],"edges":[["A","B"]],"type":"undirected"}

Yes, the two graphs are isomorphic.

Find the most influential nodes in this graph: {"nodes":["A","B","C"],"edges":[["A","B"],["B","C"]],"type":"directed"}

Node 'B' has the highest centrality as it acts as a bridge between 'A' and 'C'.

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.

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