| name | graph-explore |
| description | Explore a graph or network dataset — structure, statistics, paths, and visualization. Use when working with DOT, GML, GraphML, or Mermaid files. |
| allowed-tools | mcp__localdata__connect_database mcp__localdata__get_graph_stats mcp__localdata__get_edges mcp__localdata__get_neighbors mcp__localdata__get_node mcp__localdata__find_path mcp__localdata__export_graph |
| argument-hint | <file-path> |
Graph Explore
Connect to a graph file, analyze its structure and key nodes, find paths, and export visualizations.
Steps
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Connect to the graph. Call connect_database with the file path from $ARGUMENTS. The tool auto-detects graph formats including DOT, GML, GraphML, and Mermaid. Note the assigned database name.
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Get graph statistics. Call get_graph_stats with the database name. Review: node count, edge count, density, whether the graph is directed or undirected, connected components count, and average degree. This gives an overview of the graph's scale and connectivity.
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Identify hub nodes. From the stats, note nodes with the highest degree (most connections). Call get_neighbors for the top 3 highest-degree nodes to understand what they connect to. These hubs are often the most important entities in the network.
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Explore structure. Call get_edges to retrieve a sample of edges. Look for patterns: are edges weighted? Do they have labels or types? Is the graph sparse or dense? Identify any isolated components or bridges.
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Inspect specific nodes. For nodes of interest (hubs or user-specified), call get_node to retrieve node attributes and metadata. Note any labels, types, or properties that provide context.
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Find paths. If there are at least two notable nodes, call find_path between them. Review the shortest path length and the intermediate nodes. This reveals how information or relationships flow through the network.
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Export a visualization. Call export_graph with a suitable format (DOT for Graphviz rendering, or the original format for round-tripping). For large graphs, suggest filtering to a subgraph around nodes of interest before exporting.
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Summarize findings. Present:
- Graph type (directed/undirected, weighted/unweighted)
- Scale: node and edge counts, density
- Key structural features: hubs, communities, bridges
- Notable paths or relationships discovered
- Suggestions for further exploration or subgraph analysis
