Fabric-Analytics-MCP

#!/usr/bin/env node "use strict"; var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) { function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } return new (P || (P = Promise))(function (resolve, reject) { function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } step((generator = generator.apply(thisArg, _arguments || [])).next()); }); }; var __generator = (this && this.__generator) || function (thisArg, body) { var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g = Object.create((typeof Iterator === "function" ? Iterator : Object).prototype); return g.next = verb(0), g["throw"] = verb(1), g["return"] = verb(2), typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g; function verb(n) { return function (v) { return step([n, v]); }; } function step(op) { if (f) throw new TypeError("Generator is already executing."); while (g && (g = 0, op[0] && (_ = 0)), _) try { if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t; if (y = 0, t) op = [op[0] & 2, t.value]; switch (op[0]) { case 0: case 1: t = op; break; case 4: _.label++; return { value: op[1], done: false }; case 5: _.label++; y = op[1]; op = [0]; continue; case 7: op = _.ops.pop(); _.trys.pop(); continue; default: if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; } if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; } if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; } if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; } if (t[2]) _.ops.pop(); _.trys.pop(); continue; } op = body.call(thisArg, _); } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; } if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true }; } }; Object.defineProperty(exports, "__esModule", { value: true }); var mcp_js_1 = require("@modelcontextprotocol/sdk/server/mcp.js"); var stdio_js_1 = require("@modelcontextprotocol/sdk/server/stdio.js"); var zod_1 = require("zod"); // Microsoft Fabric Analytics MCP Server // This server provides analytics capabilities for Microsoft Fabric data platform var server = new mcp_js_1.McpServer({ name: "fabric-analytics", version: "1.0.0", capabilities: { resources: {}, tools: {}, }, }, { capabilities: { logging: {} } }); // Schemas for input validation var DatasetQuerySchema = zod_1.z.object({ workspaceId: zod_1.z.string().describe("Microsoft Fabric workspace ID"), datasetName: zod_1.z.string().describe("Name of the dataset to query"), query: zod_1.z.string().describe("SQL or KQL query to execute"), }); var MetricsAnalysisSchema = zod_1.z.object({ workspaceId: zod_1.z.string().describe("Microsoft Fabric workspace ID"), itemId: zod_1.z.string().describe("Item ID (dataset, report, etc.)"), timeRange: zod_1.z.enum(["1h", "24h", "7d", "30d"]).describe("Time range for metrics"), metrics: zod_1.z.array(zod_1.z.string()).describe("List of metrics to analyze"), }); var DataModelSchema = zod_1.z.object({ workspaceId: zod_1.z.string().describe("Microsoft Fabric workspace ID"), itemId: zod_1.z.string().describe("Item ID to analyze"), }); // Helper function to simulate Microsoft Fabric API calls // In a real implementation, these would call actual Microsoft Fabric REST APIs function simulateFabricApiCall(endpoint, data) { return __awaiter(this, void 0, void 0, function () { return __generator(this, function (_a) { switch (_a.label) { case 0: // Simulate API delay return [4 /*yield*/, new Promise(function (resolve) { return setTimeout(resolve, 100); })]; case 1: // Simulate API delay _a.sent(); // Return simulated data based on endpoint switch (endpoint) { case "query-dataset": return [2 /*return*/, { status: "success", rows: [ { id: 1, name: "Sample Product A", sales: 15000, region: "North America" }, { id: 2, name: "Sample Product B", sales: 12000, region: "Europe" }, { id: 3, name: "Sample Product C", sales: 8000, region: "Asia Pacific" }, ], totalRows: 3, executionTime: "0.45s" }]; case "get-metrics": return [2 /*return*/, { status: "success", metrics: { totalQueries: 1247, avgResponseTime: "2.3s", errorRate: "0.02%", activeUsers: 45, dataRefreshCount: 12 }, timeRange: data.timeRange, timestamp: new Date().toISOString() }]; case "analyze-model": return [2 /*return*/, { status: "success", modelInfo: { tables: 8, relationships: 12, measures: 25, columns: 156, dataSize: "2.4 GB", lastRefresh: "2025-06-18T10:30:00Z" }, recommendations: [ "Consider partitioning the Sales table for better performance", "Add indexes on frequently queried date columns", "Review unused calculated columns in Customer table" ] }]; default: return [2 /*return*/, { status: "error", message: "Unknown endpoint" }]; } return [2 /*return*/]; } }); }); } // Tool: Query Dataset server.tool("query-fabric-dataset", "Execute a query against a Microsoft Fabric dataset", { workspaceId: zod_1.z.string().describe("Microsoft Fabric workspace ID"), datasetName: zod_1.z.string().describe("Name of the dataset to query"), query: zod_1.z.string().describe("SQL or KQL query to execute"), }, function (_a) { return __awaiter(void 0, [_a], void 0, function (_b) { var result, formattedResults, error_1; var workspaceId = _b.workspaceId, datasetName = _b.datasetName, query = _b.query; return __generator(this, function (_c) { switch (_c.label) { case 0: _c.trys.push([0, 2, , 3]); return [4 /*yield*/, simulateFabricApiCall("query-dataset", { workspaceId: workspaceId, datasetName: datasetName, query: query })]; case 1: result = _c.sent(); if (result.status === "error") { return [2 /*return*/, { content: [ { type: "text", text: "Error executing query: ".concat(result.message) } ] }]; } formattedResults = result.rows.map(function (row, index) { return "Row ".concat(index + 1, ": ").concat(Object.entries(row).map(function (_a) { var key = _a[0], value = _a[1]; return "".concat(key, ": ").concat(value); }).join(", ")); }).join("\n"); return [2 /*return*/, { content: [ { type: "text", text: "Query Results for dataset \"".concat(datasetName, "\":\n\n").concat(formattedResults, "\n\nTotal Rows: ").concat(result.totalRows, "\nExecution Time: ").concat(result.executionTime) } ] }]; case 2: error_1 = _c.sent(); return [2 /*return*/, { content: [ { type: "text", text: "Failed to execute query: ".concat(error_1 instanceof Error ? error_1.message : String(error_1)) } ] }]; case 3: return [2 /*return*/]; } }); }); }); // Tool: Get Analytics Metrics server.tool("get-fabric-metrics", "Retrieve analytics metrics for Microsoft Fabric items", { workspaceId: zod_1.z.string().describe("Microsoft Fabric workspace ID"), itemId: zod_1.z.string().describe("Item ID (dataset, report, etc.)"), timeRange: zod_1.z.enum(["1h", "24h", "7d", "30d"]).describe("Time range for metrics"), metrics: zod_1.z.array(zod_1.z.string()).describe("List of metrics to analyze"), }, function (_a) { return __awaiter(void 0, [_a], void 0, function (_b) { var result, metricsText, error_2; var workspaceId = _b.workspaceId, itemId = _b.itemId, timeRange = _b.timeRange, metrics = _b.metrics; return __generator(this, function (_c) { switch (_c.label) { case 0: _c.trys.push([0, 2, , 3]); return [4 /*yield*/, simulateFabricApiCall("get-metrics", { workspaceId: workspaceId, itemId: itemId, timeRange: timeRange, metrics: metrics })]; case 1: result = _c.sent(); if (result.status === "error") { return [2 /*return*/, { content: [ { type: "text", text: "Error retrieving metrics: ".concat(result.message) } ] }]; } metricsText = Object.entries(result.metrics) .map(function (_a) { var key = _a[0], value = _a[1]; return "".concat(key, ": ").concat(value); }) .join("\n"); return [2 /*return*/, { content: [ { type: "text", text: "Analytics Metrics (".concat(timeRange, "):\n\n").concat(metricsText, "\n\nTimestamp: ").concat(result.timestamp) } ] }]; case 2: error_2 = _c.sent(); return [2 /*return*/, { content: [ { type: "text", text: "Failed to retrieve metrics: ".concat(error_2 instanceof Error ? error_2.message : String(error_2)) } ] }]; case 3: return [2 /*return*/]; } }); }); }); // Tool: Analyze Data Model server.tool("analyze-fabric-model", "Analyze a Microsoft Fabric data model and get optimization recommendations", { workspaceId: zod_1.z.string().describe("Microsoft Fabric workspace ID"), itemId: zod_1.z.string().describe("Item ID to analyze"), }, function (_a) { return __awaiter(void 0, [_a], void 0, function (_b) { var result, modelInfoText, recommendationsText, error_3; var workspaceId = _b.workspaceId, itemId = _b.itemId; return __generator(this, function (_c) { switch (_c.label) { case 0: _c.trys.push([0, 2, , 3]); return [4 /*yield*/, simulateFabricApiCall("analyze-model", { workspaceId: workspaceId, itemId: itemId })]; case 1: result = _c.sent(); if (result.status === "error") { return [2 /*return*/, { content: [ { type: "text", text: "Error analyzing model: ".concat(result.message) } ] }]; } modelInfoText = Object.entries(result.modelInfo) .map(function (_a) { var key = _a[0], value = _a[1]; return "".concat(key, ": ").concat(value); }) .join("\n"); recommendationsText = result.recommendations .map(function (rec, index) { return "".concat(index + 1, ". ").concat(rec); }) .join("\n"); return [2 /*return*/, { content: [ { type: "text", text: "Data Model Analysis:\n\n".concat(modelInfoText, "\n\nRecommendations:\n").concat(recommendationsText) } ] }]; case 2: error_3 = _c.sent(); return [2 /*return*/, { content: [ { type: "text", text: "Failed to analyze model: ".concat(error_3 instanceof Error ? error_3.message : String(error_3)) } ] }]; case 3: return [2 /*return*/]; } }); }); }); // Tool: Generate Analytics Report server.tool("generate-fabric-report", "Generate a comprehensive analytics report for Microsoft Fabric workspace", { workspaceId: zod_1.z.string().describe("Microsoft Fabric workspace ID"), reportType: zod_1.z.enum(["performance", "usage", "health", "summary"]).describe("Type of report to generate"), }, function (_a) { return __awaiter(void 0, [_a], void 0, function (_b) { var reportContent; var workspaceId = _b.workspaceId, reportType = _b.reportType; return __generator(this, function (_c) { try { reportContent = ""; switch (reportType) { case "performance": reportContent = "Performance Report for Workspace ".concat(workspaceId, "\n \n\uD83D\uDCCA Query Performance:\n- Average response time: 2.3 seconds\n- 95th percentile: 5.1 seconds\n- Slowest queries: 3 identified\n- Query optimization opportunities: 5\n\n\uD83D\uDD04 Data Refresh Performance:\n- Last refresh: Success (12 minutes ago)\n- Average refresh time: 8.5 minutes\n- Failed refreshes (24h): 0\n- Refresh schedule adherence: 98.5%\n\n\uD83D\uDCBE Storage Performance:\n- Data compression ratio: 85%\n- Index effectiveness: Good\n- Partition health: Optimal"); break; case "usage": reportContent = "Usage Report for Workspace ".concat(workspaceId, "\n \n\uD83D\uDC65 User Activity:\n- Active users (24h): 45\n- Total sessions: 156\n- Peak usage time: 10:00 AM - 12:00 PM\n- Most accessed reports: Sales Dashboard, Financial Overview\n\n\uD83D\uDCC8 Data Consumption:\n- Queries executed: 1,247\n- Data processed: 15.6 GB\n- Most queried tables: Sales, Customer, Product\n- API calls: 892"); break; case "health": reportContent = "Health Report for Workspace ".concat(workspaceId, "\n \n\u2705 System Status: Healthy\n- All services operational\n- No critical alerts\n- Data freshness: Current\n\n\u26A0\uFE0F Warnings:\n- Dataset 'Marketing Analytics' approaching capacity limit\n- Scheduled refresh for 'Customer360' delayed by 15 minutes\n\n\uD83D\uDD27 Maintenance Recommendations:\n- Update connection credentials for 'SalesDB' (expires in 7 days)\n- Review unused datasets (3 identified)\n- Optimize large tables for better performance"); break; case "summary": reportContent = "Executive Summary for Workspace ".concat(workspaceId, "\n \n\uD83D\uDCCA Key Metrics:\n- Total Datasets: 12\n- Active Reports: 8\n- Data Volume: 2.4 GB\n- Monthly Growth: +12%\n\n\uD83D\uDC65 User Engagement:\n- Daily Active Users: 45\n- Report Views: 1,856\n- User Satisfaction: 4.2/5\n\n\uD83D\uDCA1 Business Impact:\n- Queries answered: 1,247\n- Decisions supported: 89\n- Time saved: ~120 hours/month\n- ROI: 340%\n\n\uD83C\uDFAF Next Steps:\n- Expand to additional departments\n- Implement real-time analytics\n- Add predictive modeling capabilities"); break; } return [2 /*return*/, { content: [ { type: "text", text: reportContent } ] }]; } catch (error) { return [2 /*return*/, { content: [ { type: "text", text: "Failed to generate report: ".concat(error instanceof Error ? error.message : String(error)) } ] }]; } return [2 /*return*/]; }); }); }); function main() { return __awaiter(this, void 0, void 0, function () { var transport; return __generator(this, function (_a) { switch (_a.label) { case 0: transport = new stdio_js_1.StdioServerTransport(); return [4 /*yield*/, server.connect(transport)]; case 1: _a.sent(); console.error("Microsoft Fabric Analytics MCP Server running on stdio"); return [2 /*return*/]; } }); }); } main().catch(function (error) { console.error("Fatal error in main():", error); process.exit(1); });