Detect Knowledge Communities

Instructions

Implementation Reference

• src/mcp-server/index.ts:1458-1471 (handler)

  The handler function for graph_communities tool - calls client.findCommunities() with the parsed args and returns the result.

  }, async (args) => {
    try {
      const result = await client.findCommunities(currentTenant(), {
        weight_threshold: args.weight_threshold,
        max_communities: args.max_communities,
        max_hops: args.max_hops,
        min_size: args.min_size,
      });
      return toolResult(result);
    } catch (err) {
      const e = err instanceof Error ? err : new Error(String(err));
      return toolError(`graph_communities failed: ${e.message}`);
    }
  });

• src/mcp-server/index.ts:1416-1457 (schema)

  The tool registration with input schema (weight_threshold, max_communities, max_hops, min_size) and description for graph_communities.

  server.registerTool("graph_communities", {
    title: "Detect Knowledge Communities",
    description:
      "Find clusters of densely-interconnected entities in the graph. Uses greedy seed-based BFS through " +
      "edges above the weight threshold — works without GDS or APOC. Each entity is assigned to at most " +
      "one community (the first that reaches it from a high-degree seed). Useful for understanding " +
      "knowledge neighbourhoods (e.g. \"everything related to infrastructure\"). " +
      "Returns at most `max_communities` clusters, each shaped `{community_id, seed: {id, name, type}, size, members: [{id, name, type}]}`, sorted by size desc; communities below `min_size` are filtered out. " +
      "Use graph_query or graph_search instead when you have a specific entity to start from.",
    inputSchema: {
      weight_threshold: z
        .number()
        .min(0)
        .max(1)
        .optional()
        .default(0.4)
        .describe("Only traverse edges with weight strictly greater than this (default 0.4)."),
      max_communities: z
        .number()
        .int()
        .min(1)
        .max(30)
        .optional()
        .default(10)
        .describe("Maximum number of communities to return (default 10)."),
      max_hops: z
        .number()
        .int()
        .min(1)
        .max(4)
        .optional()
        .default(3)
        .describe("BFS depth from each seed (default 3, capped at 4)."),
      min_size: z
        .number()
        .int()
        .min(2)
        .optional()
        .default(2)
        .describe("Minimum members for a community to be returned (default 2)."),
    },
    annotations: { readOnlyHint: true },

• src/mcp-server/index.ts:1416-1471 (registration)

  Registration of graph_communities tool via server.registerTool() with the name 'graph_communities'.

  server.registerTool("graph_communities", {
    title: "Detect Knowledge Communities",
    description:
      "Find clusters of densely-interconnected entities in the graph. Uses greedy seed-based BFS through " +
      "edges above the weight threshold — works without GDS or APOC. Each entity is assigned to at most " +
      "one community (the first that reaches it from a high-degree seed). Useful for understanding " +
      "knowledge neighbourhoods (e.g. \"everything related to infrastructure\"). " +
      "Returns at most `max_communities` clusters, each shaped `{community_id, seed: {id, name, type}, size, members: [{id, name, type}]}`, sorted by size desc; communities below `min_size` are filtered out. " +
      "Use graph_query or graph_search instead when you have a specific entity to start from.",
    inputSchema: {
      weight_threshold: z
        .number()
        .min(0)
        .max(1)
        .optional()
        .default(0.4)
        .describe("Only traverse edges with weight strictly greater than this (default 0.4)."),
      max_communities: z
        .number()
        .int()
        .min(1)
        .max(30)
        .optional()
        .default(10)
        .describe("Maximum number of communities to return (default 10)."),
      max_hops: z
        .number()
        .int()
        .min(1)
        .max(4)
        .optional()
        .default(3)
        .describe("BFS depth from each seed (default 3, capped at 4)."),
      min_size: z
        .number()
        .int()
        .min(2)
        .optional()
        .default(2)
        .describe("Minimum members for a community to be returned (default 2)."),
    },
    annotations: { readOnlyHint: true },
  }, async (args) => {
    try {
      const result = await client.findCommunities(currentTenant(), {
        weight_threshold: args.weight_threshold,
        max_communities: args.max_communities,
        max_hops: args.max_hops,
        min_size: args.min_size,
      });
      return toolResult(result);
    } catch (err) {
      const e = err instanceof Error ? err : new Error(String(err));
      return toolError(`graph_communities failed: ${e.message}`);
    }
  });

• src/shared/neo4j-client.ts:2161-2297 (helper)

  The findCommunities() method on Neo4jClient which implements greedy seed-based BFS clustering using pure Cypher (no GDS/APOC).

  // ─── Communities ───
  // Greedy seed-based BFS clustering. No GDS/APOC required — works on Aura Free.
  // Algorithm:
  //   1. Rank entities by strong-edge degree
  //   2. Take the highest-degree unassigned entity as a seed
  //   3. BFS through edges with weight > threshold up to max_hops
  //   4. Assign all reached entities to this community
  //   5. Repeat until max_communities reached or no more high-degree seeds
  
  async findCommunities(tenantId: string, options: {
    weight_threshold?: number;
    max_communities?: number;
    max_hops?: number;
    min_size?: number;
  } = {}): Promise<{
    communities: Array<{
      id: number;
      seed_name: string;
      seed_id: string;
      member_count: number;
      dominant_type: string;
      members: Array<{ id: string; name: string; type: string }>;
    }>;
    coverage: {
      total_entities: number;
      assigned: number;
      unassigned: number;
    };
  }> {
    const threshold = options.weight_threshold ?? 0.4;
    const maxCommunities = options.max_communities ?? 10;
    const maxHops = Math.max(1, Math.min(options.max_hops ?? 3, 4));
    const minSize = options.min_size ?? 2;
  
    // Step 1: rank nodes by strong-edge degree (tenant-scoped)
    const hubRows = await this.run(
      `
      MATCH (n:Entity {tenant_id: $tenantId})-[r]-(other:Entity {tenant_id: $tenantId})
      WHERE r.weight > $threshold
      WITH n, count(r) AS degree
      WHERE degree >= 2
      RETURN n.id AS id,
             n.name AS name,
             [l IN labels(n) WHERE l <> 'Entity'][0] AS type,
             degree
      ORDER BY degree DESC
      LIMIT 100
      `,
      { tenantId, threshold },
    );
  
    // Total entity count for coverage stats (tenant-scoped)
    const totalRows = await this.run(
      `MATCH (n:Entity {tenant_id: $tenantId}) RETURN count(n) AS total`,
      { tenantId },
    );
    const totalEntities = Number(totalRows[0]?.["total"] ?? 0);
  
    const assigned = new Set<string>();
    const communities: Array<{
      id: number;
      seed_name: string;
      seed_id: string;
      member_count: number;
      dominant_type: string;
      members: Array<{ id: string; name: string; type: string }>;
    }> = [];
  
    for (const hub of hubRows) {
      if (communities.length >= maxCommunities) break;
      const hubId = String(hub["id"]);
      if (assigned.has(hubId)) continue;
  
      // BFS: variable-length path from seed, all relationships above threshold,
      // confined to this tenant's subgraph. Path is bound to a variable so we
      // can pass it to nodes() — passing the pattern directly is List<Path>.
      const memberRows = await this.run(
        `
        MATCH (seed:Entity {tenant_id: $tenantId, id: $seedId})
        MATCH path = (seed)-[r*1..${maxHops}]-(m:Entity)
        WHERE ALL(node IN nodes(path) WHERE node.tenant_id = $tenantId)
          AND ALL(rel IN r WHERE rel.weight > $threshold)
        RETURN DISTINCT m.id AS id,
               m.name AS name,
               [l IN labels(m) WHERE l <> 'Entity'][0] AS type
        `,
        { tenantId, seedId: hubId, threshold },
      );
  
      const seedRow = {
        id: hubId,
        name: String(hub["name"] ?? hubId),
        type: String(hub["type"] ?? "?"),
      };
  
      const members = [seedRow];
      const seenInCluster = new Set<string>([hubId]);
      for (const row of memberRows) {
        const id = String(row["id"]);
        if (assigned.has(id) || seenInCluster.has(id)) continue;
        seenInCluster.add(id);
        members.push({
          id,
          name: String(row["name"] ?? id),
          type: String(row["type"] ?? "?"),
        });
      }
  
      if (members.length < minSize) continue;
  
      // Compute dominant type
      const typeCounts: Record<string, number> = {};
      for (const m of members) typeCounts[m.type] = (typeCounts[m.type] ?? 0) + 1;
      const dominantType = Object.entries(typeCounts).sort((a, b) => b[1] - a[1])[0]?.[0] ?? "?";
  
      // Mark these as assigned (greedy: each entity belongs to the first community that grabs it)
      for (const m of members) assigned.add(m.id);
  
      communities.push({
        id: communities.length + 1,
        seed_name: seedRow.name,
        seed_id: seedRow.id,
        member_count: members.length,
        dominant_type: dominantType,
        members: members.slice(0, 30),
      });
    }
  
    return {
      communities,
      coverage: {
        total_entities: totalEntities,
        assigned: assigned.size,
        unassigned: totalEntities - assigned.size,
      },
    };
  }