Grafeo MCP Server

Run PageRank and return the top-k most important nodes. PageRank assigns every node a score proportional to how many (and how important) other nodes link to it. Higher scores mean more connected / influential nodes. Use this tool when: you want to find the most important or central nodes in the graph based on link structure. Do NOT use this for: finding similar nodes by content (use vector_search) or for finding shortest paths (use dijkstra). Args: damping: Probability of following a link vs. teleporting (default 0.85). max_iterations: Upper bound on convergence iterations (default 100). tolerance: Convergence threshold (default 1e-6). top_k: How many top-ranked nodes to return (default 20). The algorithm always scores every node, but only the top-k are returned to keep the output manageable. Returns: JSON array of {node_id, score, labels, properties} sorted by score descending. Output is truncated if it exceeds the token budget. Error recovery: If this returns an error, verify the graph is non-empty with graph_info. PageRank requires at least one edge.

Find the shortest weighted path between two nodes (Dijkstra's algorithm). Returns the total distance and the sequence of nodes along the path. Use this tool when: you need the shortest or cheapest path between two known nodes. Do NOT use this for: discovering important nodes (use pagerank) or finding similar content (use vector_search). Args: source_id: Starting node ID. target_id: Destination node ID. weight_property: Edge property to use as weight (e.g. "distance", "cost"). If None every edge has weight 1.0. Returns: JSON object with {distance, path: [{node_id, labels, properties}, ...]}. Returns an error message if the nodes are unreachable. Error recovery: If the result is null/unreachable, check that both node IDs exist (use get_node) and that there is a connecting path in the graph.

Detect communities using the Louvain modularity-optimization algorithm. Groups densely-connected nodes into communities. Higher resolution values produce more (smaller) communities; lower values produce fewer (larger) communities. Use this tool when: you want to discover clusters or groups in the graph. Do NOT use this for: finding paths (use dijkstra) or ranking nodes (use pagerank). Args: resolution: Resolution parameter (default 1.0). Values > 1 favor smaller communities, values < 1 favor larger ones. Returns: JSON object with {modularity, num_communities, communities} where communities maps community_id -> list of node summaries. Output is truncated if it exceeds the token budget. Error recovery: If this returns 0 communities, the graph may have no edges. Check with graph_info.

Compute betweenness centrality for all nodes (Brandes' algorithm). Betweenness centrality measures how often a node lies on the shortest path between other node pairs. High-betweenness nodes are "bridges" that connect different parts of the graph. Use this tool when: you want to find bridge or bottleneck nodes. Do NOT use this for: finding the most linked-to nodes (use pagerank) or finding communities (use louvain). Args: normalized: If True, normalize scores by 2/((n-1)(n-2)) so they fall in [0, 1] (default True). top_k: Number of top-ranked nodes to return (default 20). Returns: JSON array of {node_id, score, labels, properties} sorted by score descending. Error recovery: If all scores are 0 the graph may be too small or disconnected.

Find connected components (treating the graph as undirected). A connected component is a maximal set of nodes such that every pair is reachable from every other by following edges in either direction. Use this tool when: you want to know how many disconnected subgraphs exist, or which nodes belong to the same component. Do NOT use this for: finding dense sub-communities (use louvain). Returns: JSON object with {num_components, components} where components maps component_id -> list of node IDs. Error recovery: If every node is its own component, the graph has no edges.

Guide an AI agent through structured exploration of the knowledge graph. The agent will systematically discover the graph's schema, sample nodes, traverse relationships, and summarize patterns.

Guide an AI agent through extracting entities and relationships from text into the graph. The agent will analyze the text, create nodes for entities, create edges for relationships, and verify the resulting graph structure.

Guide an AI agent through analytical exploration of the graph structure. The agent will run community detection, PageRank, and neighborhood traversals to produce a narrative about the graph's structure and important entities.

Guide an AI agent through vector-powered semantic search on the graph. The agent will perform similarity search, explore neighborhoods of results, and synthesize findings.