Langfuse MCP Server

import { z } from 'zod'; import { LangfuseAnalyticsClient } from '../langfuse-client.js'; import { MetricsResponse } from '../types.js'; export const getMetricsSchema = z.object({ from: z.string().datetime(), to: z.string().datetime(), view: z.enum(['traces', 'observations']).default('traces'), metrics: z.array(z.object({ measure: z.string(), aggregation: z.string(), })).default([ { measure: 'totalCost', aggregation: 'sum' }, { measure: 'totalTokens', aggregation: 'sum' }, { measure: 'count', aggregation: 'count' }, ]), dimensions: z.array(z.object({ field: z.string(), })).optional(), filters: z.array(z.object({ column: z.string(), operator: z.string(), value: z.any(), type: z.string().optional(), })).optional(), environment: z.string().optional(), }); export async function getMetrics( client: LangfuseAnalyticsClient, args: z.infer<typeof getMetricsSchema> ) { const filters: any[] = args.filters || []; // Add environment filter if specified if (args.environment) { filters.push({ column: 'environment', operator: 'equals', value: args.environment, type: 'string', }); } const response = await client.getMetrics({ view: args.view, from: args.from, to: args.to, metrics: args.metrics, dimensions: args.dimensions, filters, }); // Process the response into a structured format const metricsResult: MetricsResponse = { projectId: client.getProjectId(), from: args.from, to: args.to, view: args.view, metrics: [], dimensions: [], }; // Parse metrics data from response if (response.data && Array.isArray(response.data)) { // Aggregate metrics across all rows const aggregatedMetrics: Record<string, number> = {}; response.data.forEach((row: any) => { args.metrics.forEach(metric => { const key = `${metric.measure}_${metric.aggregation}`; // Metrics API returns aggregated field names like 'totalCost_sum', 'count_count' const aggregatedFieldName = `${metric.measure}_${metric.aggregation}`; if (row[aggregatedFieldName] !== undefined) { if (metric.aggregation === 'sum' || metric.aggregation === 'count') { aggregatedMetrics[key] = (aggregatedMetrics[key] || 0) + (row[aggregatedFieldName] || 0); } else if (metric.aggregation === 'avg') { // For average, we'll need to handle this differently aggregatedMetrics[key] = row[aggregatedFieldName] || 0; } } }); }); // Convert aggregated metrics to result format metricsResult.metrics = Object.entries(aggregatedMetrics).map(([key, value]) => { const [measure, aggregation] = key.split('_'); return { measure, aggregation, value }; }); // Extract dimension data if present if (args.dimensions && response.data.length > 0) { const dimensions = new Set<string>(); response.data.forEach((row: any) => { args.dimensions?.forEach(dim => { if (row[dim.field] !== undefined) { dimensions.add(`${dim.field}:${row[dim.field]}`); } }); }); metricsResult.dimensions = Array.from(dimensions).map(dimStr => { const [field, value] = dimStr.split(':'); return { field, value }; }); } } return { content: [ { type: 'text' as const, text: JSON.stringify(metricsResult, null, 2), }, ], }; }