fixed formatting

Author: ChernovAndrey

src/index.ts

@@ -121,12 +121,39 @@ server.tool(
   'forecast',
   'Perform time series forecasting using FAIM platform. Supports both point forecasting (single value) and probabilistic forecasting (confidence intervals). Can handle univariate and multivariate time series data. Currently supported models: Chronos2 (default, recommended for multivariate) and TiRex (fast, univariate only).',
   {
-    model: z.enum(['chronos2', 'tirex']).describe('The forecasting model to use. Chronos2: State-of-the-art, supports univariate/multivariate, custom quantiles. TiRex: Fast alternative for univariate only, uses fixed quantiles [0.1,0.2,...,0.9], custom quantiles parameter ignored.'),
-    x: z.any().describe('Time series data to forecast from. Can be a 1D array (single series), 2D array (multiple series/batch or multivariate per model), or 3D array (batch, sequence, features).'),
-    horizon: z.number().describe('Number of time steps to forecast into the future. Must be a positive integer. Example: 10 means predict the next 10 steps.'),
-    output_type: z.enum(['point', 'quantiles']).optional().describe('Type of forecast output. "point" = single value per step (fastest). "quantiles" = confidence intervals (use for uncertainty estimation). Default: "point".'),
-    quantiles: z.array(z.number()).optional().describe('Custom quantile levels to compute (only used with output_type="quantiles" and Chronos2 model). For TiRex, this parameter is ignored and fixed quantiles [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9] are always returned. Values must be between 0 and 1. Example: [0.1, 0.5, 0.9] for 10th, 50th, 90th percentiles.'),
-    is_multivariate: z.boolean().optional().describe('For 2D input arrays only with Chronos2: interpret as multivariate time series (true) or batch of univariate series (false, default). Ignored for 1D arrays, 3D arrays, and TiRex model.'),
+    model: z
+      .enum(['chronos2', 'tirex'])
+      .describe(
+        'The forecasting model to use. Chronos2: State-of-the-art, supports univariate/multivariate, custom quantiles. TiRex: Fast alternative for univariate only, uses fixed quantiles [0.1,0.2,...,0.9], custom quantiles parameter ignored.'
+      ),
+    x: z
+      .any()
+      .describe(
+        'Time series data to forecast from. Can be a 1D array (single series), 2D array (multiple series/batch or multivariate per model), or 3D array (batch, sequence, features).'
+      ),
+    horizon: z
+      .number()
+      .describe(
+        'Number of time steps to forecast into the future. Must be a positive integer. Example: 10 means predict the next 10 steps.'
+      ),
+    output_type: z
+      .enum(['point', 'quantiles'])
+      .optional()
+      .describe(
+        'Type of forecast output. "point" = single value per step (fastest). "quantiles" = confidence intervals (use for uncertainty estimation). Default: "point".'
+      ),
+    quantiles: z
+      .array(z.number())
+      .optional()
+      .describe(
+        'Custom quantile levels to compute (only used with output_type="quantiles" and Chronos2 model). For TiRex, this parameter is ignored and fixed quantiles [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9] are always returned. Values must be between 0 and 1. Example: [0.1, 0.5, 0.9] for 10th, 50th, 90th percentiles.'
+      ),
+    is_multivariate: z
+      .boolean()
+      .optional()
+      .describe(
+        'For 2D input arrays only with Chronos2: interpret as multivariate time series (true) or batch of univariate series (false, default). Ignored for 1D arrays, 3D arrays, and TiRex model.'
+      ),
   },
   async ({ model, x, horizon, output_type, quantiles, is_multivariate }: any) => {
     const result = await forecast({
@@ -176,7 +203,6 @@ async function main(): Promise<void> {
     // Connect server to transport
     // The transport handles all JSON-RPC protocol details
     await server.connect(transport);
-
   } catch (error) {
     console.error('[MCP] Failed to start server:', error);
     process.exit(1);

src/tools/forecast.ts

@@ -37,9 +37,7 @@ import { transformError } from '../utils/errors.js';
  * @param request - Pre-validated forecast request
  * @returns {Promise<ToolResult<ForecastResponse>>} Either success with predictions or error
  */
-export async function forecast(
-  request: unknown
-): Promise<ToolResult<ForecastResponse>> {
+export async function forecast(request: unknown): Promise<ToolResult<ForecastResponse>> {
   try {
     // Validate request before processing
     const validationError = validateForecastRequest(request);
@@ -76,7 +74,6 @@ export async function forecast(
     );
     const outputShape = getArrayShape(normalizedX);
 
-
     // Get the FAIM client (singleton initialized at server startup)
     const client = getClient();
 
@@ -151,7 +148,6 @@ export async function forecast(
       },
     };
 
-
     return {
       success: true,
       data: response,

src/types.ts

@@ -148,9 +148,7 @@ export interface ForecastRequest {
  * For point forecasts: { point: number[][][] }
  * For quantile forecasts: { quantiles: number[][][][] }
  */
-export type ForecastOutput =
-  | { point: number[][][] }
-  | { quantiles: number[][][][] };
+export type ForecastOutput = { point: number[][][] } | { quantiles: number[][][][] };
 
 /**
  * Complete response from a successful forecast operation

src/utils/client.ts

@@ -65,7 +65,7 @@ export function initializeClient(): void {
     if (!apiKey || apiKey.trim().length === 0) {
       initializationError = new Error(
         'FAIM_API_KEY environment variable is not set. ' +
-        'Please set it before starting the MCP server.'
+          'Please set it before starting the MCP server.'
       );
       throw initializationError;
     }
@@ -86,7 +86,6 @@ export function initializeClient(): void {
       // Users can override via FAIM_API_BASE_URL environment variable
       baseUrl: process.env.FAIM_API_BASE_URL,
     });
-
   } catch (error) {
     // Store the error for later retrieval
     initializationError = error instanceof Error ? error : new Error(String(error));
@@ -122,9 +121,7 @@ export function getClient(): FaimClient {
 
   // If initialization hasn't been attempted, indicate this is a startup issue
   if (!clientInstance) {
-    throw new Error(
-      'FAIM client not initialized. Call initializeClient() during server startup.'
-    );
+    throw new Error('FAIM client not initialized. Call initializeClient() during server startup.');
   }
 
   return clientInstance;

src/utils/errors.ts

@@ -188,7 +188,7 @@ function transformJavaScriptError(
     logError('VALIDATION_ERROR', message, error);
     return {
       error_code: 'INVALID_PARAMETER',
-      message: message,  // Use the actual validation message
+      message: message, // Use the actual validation message
       details: context?.operation ? `Operation: ${context.operation}` : undefined,
       field: context?.field,
     };
@@ -253,9 +253,7 @@ function isNetworkError(message: string): boolean {
     'connect',
   ];
 
-  return networkPatterns.some((pattern) =>
-    message.toLowerCase().includes(pattern.toLowerCase())
-  );
+  return networkPatterns.some((pattern) => message.toLowerCase().includes(pattern.toLowerCase()));
 }
 
 /**
@@ -264,15 +262,9 @@ function isNetworkError(message: string): boolean {
  * @internal Helper for error classification
  */
 function isTimeoutError(message: string): boolean {
-  const timeoutPatterns = [
-    'timeout',
-    'timed out',
-    'deadline exceeded',
-  ];
+  const timeoutPatterns = ['timeout', 'timed out', 'deadline exceeded'];
 
-  return timeoutPatterns.some((pattern) =>
-    message.toLowerCase().includes(pattern.toLowerCase())
-  );
+  return timeoutPatterns.some((pattern) => message.toLowerCase().includes(pattern.toLowerCase()));
 }
 
 /**
@@ -284,12 +276,7 @@ function isTimeoutError(message: string): boolean {
  * @internal Helper for error classification
  */
 function isValidationError(message: string): boolean {
-  const validationPatterns = [
-    'cannot be empty',
-    'must be',
-    'invalid',
-    'expected',
-  ];
+  const validationPatterns = ['cannot be empty', 'must be', 'invalid', 'expected'];
 
   return validationPatterns.some((pattern) =>
     message.toLowerCase().includes(pattern.toLowerCase())
@@ -356,11 +343,7 @@ function logError(code: string, message: string, originalError: unknown): void {
   }
 
   // In production, log only important errors
-  const importantCodes = [
-    'AUTHENTICATION_FAILED',
-    'INTERNAL_SERVER_ERROR',
-    'RESOURCE_EXHAUSTED',
-  ];
+  const importantCodes = ['AUTHENTICATION_FAILED', 'INTERNAL_SERVER_ERROR', 'RESOURCE_EXHAUSTED'];
 
   if (importantCodes.includes(code)) {
     console.error(`[${code}] ${message}`);

src/utils/validation.ts

@@ -197,7 +197,9 @@ export function validateForecastRequest(request: unknown): ErrorResponse | null
  *
  * @internal Internal helper for validateForecastRequest
  */
-function validateArrayInput(x: unknown): { error_code: string; message: string; details?: string } | null {
+function validateArrayInput(
+  x: unknown
+): { error_code: string; message: string; details?: string } | null {
   if (!Array.isArray(x)) {
     return {
       error_code: 'INVALID_PARAMETER',

tests/integration/forecast.integration.test.ts

@@ -347,7 +347,13 @@ describe('Forecast Integration Tests', () => {
   it('should forecast with 3D multivariate array (Chronos2)', async () => {
     const request = {
       model: 'chronos2',
-      x: [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]],
+      x: [
+        [
+          [1.0, 2.0, 3.0],
+          [4.0, 5.0, 6.0],
+          [7.0, 8.0, 9.0],
+        ],
+      ],
       horizon: 1,
       output_type: 'point' as const,
     };
@@ -875,9 +881,7 @@ describe('Forecast Integration Tests', () => {
     expect(result2.success).toBe(true);
     if (result1.success && result2.success) {
       // Results should be identical for same input
-      expect(JSON.stringify(result1.data.forecast)).toBe(
-        JSON.stringify(result2.data.forecast)
-      );
+      expect(JSON.stringify(result1.data.forecast)).toBe(JSON.stringify(result2.data.forecast));
     }
   });
 
@@ -974,4 +978,4 @@ describe('Forecast Integration Tests', () => {
       expect(result.data.forecast.point).toBeDefined();
     }
   });
-});
+});

tests/tools/forecast.test.ts

@@ -146,15 +146,25 @@ describe('forecast tool', () => {
   it('should accept 2D array input (multivariate)', async () => {
     const request = {
       model: 'chronos2',
-      x: [[1, 2], [3, 4], [5, 6]],
+      x: [
+        [1, 2],
+        [3, 4],
+        [5, 6],
+      ],
       horizon: 10,
       is_multivariate: true,
     };
 
     const result = await forecast(request);
 
     expect(result.success).toBe(true);
-    const expectedX = [[[1, 2], [3, 4], [5, 6]]];
+    const expectedX = [
+      [
+        [1, 2],
+        [3, 4],
+        [5, 6],
+      ],
+    ];
     expect(mockClient.forecastChronos2).toHaveBeenCalledWith(
       expect.objectContaining({ x: expectedX })
     );

tests/utils/validation.test.ts

@@ -77,7 +77,11 @@ describe('validateForecastRequest', () => {
   it('should accept 2D array input (multivariate)', () => {
     const request = {
       model: 'chronos2',
-      x: [[1, 2], [3, 4], [5, 6]],
+      x: [
+        [1, 2],
+        [3, 4],
+        [5, 6],
+      ],
       horizon: 10,
     };
 
@@ -397,7 +401,11 @@ describe('normalizeInput', () => {
     });
 
     it('should flatten 2D array for TiRex model (is_multivariate ignored)', () => {
-      const input = [[1, 2], [3, 4], [5, 6]];
+      const input = [
+        [1, 2],
+        [3, 4],
+        [5, 6],
+      ];
       const normalized = normalizeInput(input, 'tirex', true); // is_multivariate=true but should be ignored for TiRex
 
       expect(normalized).toHaveLength(1); // batch size 1
@@ -409,7 +417,11 @@ describe('normalizeInput', () => {
 
   describe('2D array handling with is_multivariate=true (Chronos2 only)', () => {
     it('should keep 2D array as multivariate for Chronos2 with is_multivariate=true', () => {
-      const input = [[1, 2], [3, 4], [5, 6]]; // shape [3, 2] -> multivariate (3 timesteps, 2 features)
+      const input = [
+        [1, 2],
+        [3, 4],
+        [5, 6],
+      ]; // shape [3, 2] -> multivariate (3 timesteps, 2 features)
       const normalized = normalizeInput(input, 'chronos2', true);
 
       expect(normalized).toHaveLength(1); // batch size 1
@@ -431,19 +443,33 @@ describe('normalizeInput', () => {
     });
 
     it('should handle many features multivariate 2D array', () => {
-      const input = [[10, 20, 30], [40, 50, 60], [70, 80, 90], [100, 110, 120]];
+      const input = [
+        [10, 20, 30],
+        [40, 50, 60],
+        [70, 80, 90],
+        [100, 110, 120],
+      ];
       const normalized = normalizeInput(input, 'chronos2', true);
 
       expect(normalized).toHaveLength(1); // batch size 1
       expect(normalized[0]).toHaveLength(4); // 4 time steps
       expect(normalized[0][0]).toHaveLength(3); // 3 features
-      expect(normalized[0]).toEqual([[10, 20, 30], [40, 50, 60], [70, 80, 90], [100, 110, 120]]);
+      expect(normalized[0]).toEqual([
+        [10, 20, 30],
+        [40, 50, 60],
+        [70, 80, 90],
+        [100, 110, 120],
+      ]);
     });
   });
 
   describe('2D array handling with is_multivariate=false (explicit batch inference)', () => {
     it('should flatten 2D array for Chronos2 with is_multivariate=false', () => {
-      const input = [[1, 2], [3, 4], [5, 6]];
+      const input = [
+        [1, 2],
+        [3, 4],
+        [5, 6],
+      ];
       const normalized = normalizeInput(input, 'chronos2', false);
 
       // With is_multivariate=false, should flatten to: [1, 2, 3, 4, 5, 6]
@@ -490,7 +516,12 @@ describe('normalizeInput', () => {
 
   describe('3D array handling with is_multivariate flag', () => {
     it('should ignore is_multivariate flag for 3D array', () => {
-      const input = [[[1, 2], [3, 4]]];
+      const input = [
+        [
+          [1, 2],
+          [3, 4],
+        ],
+      ];
       const normalized = normalizeInput(input, 'chronos2', true);
 
       // is_multivariate should be ignored for 3D arrays
@@ -544,12 +575,25 @@ describe('getArrayShape', () => {
   });
 
   it('should get shape of 2D array', () => {
-    const shape = getArrayShape([[1, 2], [3, 4], [5, 6]]);
+    const shape = getArrayShape([
+      [1, 2],
+      [3, 4],
+      [5, 6],
+    ]);
     expect(shape).toEqual([3, 2]);
   });
 
   it('should get shape of 3D array', () => {
-    const shape = getArrayShape([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]);
+    const shape = getArrayShape([
+      [
+        [1, 2],
+        [3, 4],
+      ],
+      [
+        [5, 6],
+        [7, 8],
+      ],
+    ]);
     expect(shape).toEqual([2, 2, 2]);
   });