to npm

Author: ChernovAndrey

README.md

@@ -53,9 +53,56 @@ This server uses the **official `@modelcontextprotocol/sdk`** package from Anthr
 - npm 10+
 - FAIM API key (set as `FAIM_API_KEY` environment variable)
 
-### Setup
+### Option 1: Install from npm (Recommended)
 
 ```bash
+npm install @faim-group/mcp
+```
+
+Then configure Claude Desktop to use it:
+
+```json
+{
+  "mcpServers": {
+    "faim": {
+      "command": "node",
+      "args": ["-e", "require('@faim-group/mcp')"],
+      "env": {
+        "FAIM_API_KEY": "your-api-key-here"
+      }
+    }
+  }
+}
+```
+
+Or if installed globally:
+
+```bash
+npm install -g @faim-group/mcp
+```
+
+Then in Claude Desktop config:
+
+```json
+{
+  "mcpServers": {
+    "faim": {
+      "command": "faim-mcp",
+      "env": {
+        "FAIM_API_KEY": "your-api-key-here"
+      }
+    }
+  }
+}
+```
+
+### Option 2: Clone and Build Locally
+
+```bash
+# Clone the repository
+git clone <repository-url>
+cd faim-mcp
+
 # Install dependencies
 npm install
 
@@ -69,6 +116,22 @@ npm test
 npm run lint
 ```
 
+Then in Claude Desktop config, use the local path:
+
+```json
+{
+  "mcpServers": {
+    "faim": {
+      "command": "node",
+      "args": ["/path/to/faim-mcp/dist/index.js"],
+      "env": {
+        "FAIM_API_KEY": "your-api-key-here"
+      }
+    }
+  }
+}
+```
+
 ## Configuration
 
 ### Environment Variables

package.json

@@ -1,6 +1,6 @@
 {
-  "name": "faim-mcp",
-  "version": "1.0.0",
+  "name": "@faim-group/mcp",
+  "version": "0.1.0",
   "description": "MCP server for FAIM forecasting SDK - Enables Claude to perform time series forecasting using Chronos2 and TiRex models",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",

src/index.ts

@@ -58,6 +58,7 @@
  * and ensuring compatibility with the MCP specification.
  */
 
+import { z } from 'zod';
 import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
 import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
 import { initializeClient } from './utils/client.js';
@@ -90,7 +91,7 @@ server.registerTool(
   {
     description:
       'List all available forecasting models and their capabilities. Returns information about Chronos2, TiRex, and other available models, including supported output types and features.',
-    inputSchema: {},
+    inputSchema: z.object({}).strict() as any,
   },
   async () => {
     const result = await listModels();
@@ -122,72 +123,23 @@ server.registerTool(
   {
     description:
       'Perform time series forecasting using FAIM models. Supports both point forecasting (single value) and probabilistic forecasting (confidence intervals). Can handle univariate and multivariate time series data.',
-    inputSchema: {
-      type: 'object' as const,
-      properties: {
-        model: {
-          type: 'string' as const,
-          enum: ['chronos2', 'tirex'],
-          description:
-            'The forecasting model to use. Chronos2 is the general-purpose model. TiRex is an alternative with different characteristics.',
-        },
-        x: {
-          description:
-            'Time series data to forecast from. Can be a 1D array (single series), 2D array (multiple series or multivariate), or 3D array. 1D example: [1,2,3,4,5]. 2D example: [[1,2],[3,4],[5,6]].',
-          oneOf: [
-            {
-              type: 'array' as const,
-              items: { type: 'number' as const },
-              description: '1D array: single univariate time series',
-            },
-            {
-              type: 'array' as const,
-              items: {
-                type: 'array' as const,
-                items: { type: 'number' as const },
-              },
-              description: '2D array: multiple timesteps with features',
-            },
-            {
-              type: 'array' as const,
-              items: {
-                type: 'array' as const,
-                items: {
-                  type: 'array' as const,
-                  items: { type: 'number' as const },
-                },
-              },
-              description: '3D array: batch of time series',
-            },
-          ],
-        },
-        horizon: {
-          type: 'number' as const,
-          description:
-            'Number of time steps to forecast into the future. Must be a positive integer. Example: 10 means predict the next 10 steps.',
-        },
-        output_type: {
-          type: 'string' as const,
-          enum: ['point', 'quantiles'],
-          default: 'point',
-          description:
-            'Type of forecast output. "point" = single value per step (fastest). "quantiles" = confidence intervals (use for uncertainty).',
-        },
-        quantiles: {
-          type: 'array' as const,
-          items: { type: 'number' as const },
-          description:
-            'Quantile levels to compute (only used with output_type="quantiles"). Values between 0 and 1. Example: [0.1, 0.5, 0.9] for 10th, 50th, 90th percentiles.',
-        },
-      },
-      required: ['model', 'x', 'horizon'] as const,
-    } as any,
+    inputSchema: z.object({
+      model: z.enum(['chronos2', 'tirex']).describe('The forecasting model to use. Chronos2 is the general-purpose model. TiRex is an alternative with different characteristics.'),
+      x: z.union([
+        z.array(z.number()).describe('1D array: single univariate time series'),
+        z.array(z.array(z.number())).describe('2D array: multiple timesteps with features'),
+        z.array(z.array(z.array(z.number()))).describe('3D array: batch of time series'),
+      ]).describe('Time series data to forecast from. Can be a 1D array (single series), 2D array (multiple series or multivariate), or 3D array.'),
+      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']).default('point').describe('Type of forecast output. "point" = single value per step (fastest). "quantiles" = confidence intervals (use for uncertainty).'),
+      quantiles: z.array(z.number()).optional().describe('Quantile levels to compute (only used with output_type="quantiles"). Values between 0 and 1. Example: [0.1, 0.5, 0.9] for 10th, 50th, 90th percentiles.'),
+    }).strict() as any,
   },
   async (args: unknown) => {
     const result = await forecast(args);
 
     if (!result.success) {
-      throw new Error(JSON.stringify(result.error));
+      throw new Error(result.error.message);
     }
 
     // Format response for MCP
@@ -217,7 +169,6 @@ async function main(): Promise<void> {
   try {
     // Initialize FAIM client before accepting requests
     initializeClient();
-    console.error('[MCP] FAIM client initialized successfully');
 
     // Create transport for stdio communication
     const transport = new StdioServerTransport();
@@ -226,8 +177,6 @@ async function main(): Promise<void> {
     // The transport handles all JSON-RPC protocol details
     await server.connect(transport);
 
-    console.error('[MCP] Server started with official @modelcontextprotocol/sdk');
-    console.error('[MCP] Waiting for requests from Claude...');
   } catch (error) {
     console.error('[MCP] Failed to start server:', error);
     process.exit(1);

src/tools/forecast.ts

@@ -45,8 +45,6 @@ export async function forecast(
     const normalizedX = normalizeInput(forecastRequest.x);
     const outputShape = getArrayShape(normalizedX);
 
-    // Log the operation for debugging
-    console.log(`Forecast request: model=${forecastRequest.model}, horizon=${forecastRequest.horizon}, output_type=${forecastRequest.output_type || 'point'}`);
 
     // Get the FAIM client (singleton initialized at server startup)
     const client = getClient();
@@ -122,17 +120,13 @@ export async function forecast(
       },
     };
 
-    // Log successful forecast
-    console.log(`Forecast completed successfully: ${duration}ms, tokens=${sdkResponse.metadata.token_count}`);
 
     return {
       success: true,
       data: response,
     };
   } catch (error) {
     // Catch all errors and transform to user-friendly format
-    console.error('Forecast tool error:', error);
-
     return {
       success: false,
       error: transformError(error, { operation: 'forecast' }),

src/utils/client.ts

@@ -87,8 +87,6 @@ export function initializeClient(): void {
       baseUrl: process.env.FAIM_API_BASE_URL,
     });
 
-    // Log successful initialization (without exposing the API key)
-    console.log('FAIM client initialized successfully');
   } catch (error) {
     // Store the error for later retrieval
     initializationError = error instanceof Error ? error : new Error(String(error));