226 lines
6.2 KiB
TypeScript
226 lines
6.2 KiB
TypeScript
import {
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McpServer,
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type ReadResourceResult,
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} from "@modelcontextprotocol/server";
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import fs from "node:fs/promises";
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import path from "node:path";
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import { z } from "zod";
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import {
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RESOURCE_MIME_TYPE,
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registerAppResource,
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registerAppTool,
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} from "@modelcontextprotocol/ext-apps/server";
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// Works both from source (server.ts) and compiled (dist/server.js)
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const DIST_DIR = import.meta.filename.endsWith(".ts")
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? path.join(import.meta.dirname, "dist")
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: import.meta.dirname;
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// Schemas - types are derived from these using z.infer
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const GetCohortDataInputSchema = z.object({
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metric: z
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.enum(["retention", "revenue", "active"])
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.optional()
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.default("retention"),
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periodType: z.enum(["monthly", "weekly"]).optional().default("monthly"),
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cohortCount: z.number().min(3).max(24).optional().default(12),
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maxPeriods: z.number().min(3).max(24).optional().default(12),
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});
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const CohortCellSchema = z.object({
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cohortIndex: z.number(),
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periodIndex: z.number(),
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retention: z.number(),
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usersRetained: z.number(),
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usersOriginal: z.number(),
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});
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const CohortRowSchema = z.object({
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cohortId: z.string(),
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cohortLabel: z.string(),
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originalUsers: z.number(),
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cells: z.array(CohortCellSchema),
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});
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const CohortDataSchema = z.object({
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cohorts: z.array(CohortRowSchema),
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periods: z.array(z.string()),
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periodLabels: z.array(z.string()),
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metric: z.string(),
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periodType: z.string(),
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generatedAt: z.string(),
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});
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// Types derived from schemas
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type CohortCell = z.infer<typeof CohortCellSchema>;
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type CohortRow = z.infer<typeof CohortRowSchema>;
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type CohortData = z.infer<typeof CohortDataSchema>;
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// Internal types (not part of API schema)
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interface RetentionParams {
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baseRetention: number;
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decayRate: number;
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floor: number;
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noise: number;
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}
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// Retention curve generator using exponential decay
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function generateRetention(period: number, params: RetentionParams): number {
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if (period === 0) return 1.0;
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const { baseRetention, decayRate, floor, noise } = params;
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const base = baseRetention * Math.exp(-decayRate * (period - 1)) + floor;
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const variation = (Math.random() - 0.5) * 2 * noise;
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return Math.max(0, Math.min(1, base + variation));
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}
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// Generate cohort data
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function generateCohortData(
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metric: string,
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periodType: string,
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cohortCount: number,
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maxPeriods: number,
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): CohortData {
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const now = new Date();
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const cohorts: CohortRow[] = [];
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const periods: string[] = [];
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const periodLabels: string[] = [];
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// Generate period headers
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for (let i = 0; i < maxPeriods; i++) {
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periods.push(`M${i}`);
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periodLabels.push(i === 0 ? "Month 0" : `Month ${i}`);
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}
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// Retention parameters vary by metric type
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const paramsMap: Record<string, RetentionParams> = {
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retention: {
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baseRetention: 0.75,
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decayRate: 0.12,
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floor: 0.08,
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noise: 0.04,
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},
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revenue: { baseRetention: 0.7, decayRate: 0.1, floor: 0.15, noise: 0.06 },
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active: { baseRetention: 0.6, decayRate: 0.18, floor: 0.05, noise: 0.05 },
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};
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const params = paramsMap[metric] ?? paramsMap.retention;
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// Generate cohorts (oldest first)
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for (let c = 0; c < cohortCount; c++) {
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const cohortDate = new Date(now);
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cohortDate.setMonth(cohortDate.getMonth() - (cohortCount - 1 - c));
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const cohortId = `${cohortDate.getFullYear()}-${String(cohortDate.getMonth() + 1).padStart(2, "0")}`;
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const cohortLabel = cohortDate.toLocaleDateString("en-US", {
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month: "short",
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year: "numeric",
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});
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// Random cohort size: 1000-5000 users
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const originalUsers = Math.floor(1000 + Math.random() * 4000);
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// Number of periods this cohort has data for (newer cohorts have fewer periods)
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const periodsAvailable = cohortCount - c;
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const cells: CohortCell[] = [];
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let previousRetention = 1.0;
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for (let p = 0; p < Math.min(periodsAvailable, maxPeriods); p++) {
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// Retention must decrease or stay same (with small exceptions for noise)
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let retention = generateRetention(p, params);
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retention = Math.min(retention, previousRetention + 0.02);
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previousRetention = retention;
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cells.push({
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cohortIndex: c,
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periodIndex: p,
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retention,
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usersRetained: Math.round(originalUsers * retention),
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usersOriginal: originalUsers,
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});
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}
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cohorts.push({ cohortId, cohortLabel, originalUsers, cells });
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}
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return {
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cohorts,
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periods,
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periodLabels,
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metric,
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periodType,
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generatedAt: new Date().toISOString(),
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};
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}
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function formatCohortSummary(data: CohortData): string {
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const avgRetention = data.cohorts
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.flatMap((c) => c.cells)
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.filter((cell) => cell.periodIndex > 0)
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.reduce((sum, cell, _, arr) => sum + cell.retention / arr.length, 0);
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return `Cohort Analysis: ${data.cohorts.length} cohorts, ${data.periods.length} periods
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Average retention: ${(avgRetention * 100).toFixed(1)}%
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Metric: ${data.metric}, Period: ${data.periodType}`;
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}
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export function createServer(): McpServer {
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const server = new McpServer({
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name: "Cohort Heatmap Server",
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version: "1.0.0",
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});
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// Register tool and resource
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const resourceUri = "ui://get-cohort-data/mcp-app.html";
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registerAppTool(
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server,
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"get-cohort-data",
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{
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title: "Get Cohort Retention Data",
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description:
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"Returns cohort retention heatmap data showing customer retention over time by signup month",
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inputSchema: GetCohortDataInputSchema,
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outputSchema: CohortDataSchema,
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_meta: { ui: { resourceUri } },
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},
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async ({ metric, periodType, cohortCount, maxPeriods }) => {
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const data = generateCohortData(
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metric,
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periodType,
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cohortCount,
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maxPeriods,
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);
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return {
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content: [{ type: "text", text: formatCohortSummary(data) }],
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structuredContent: data,
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};
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},
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);
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registerAppResource(
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server,
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resourceUri,
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resourceUri,
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{ mimeType: RESOURCE_MIME_TYPE },
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async (): Promise<ReadResourceResult> => {
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const html = await fs.readFile(
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path.join(DIST_DIR, "mcp-app.html"),
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"utf-8",
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);
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return {
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contents: [
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{
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uri: resourceUri,
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mimeType: RESOURCE_MIME_TYPE,
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text: html,
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},
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],
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};
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},
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);
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return server;
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}
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