Tusk Drift MCP

import type { Tool } from "@modelcontextprotocol/sdk/types.js"; import type { TuskDriftApiClient } from "../apiClient.js"; import { aggregateSpansInputSchema, type AggregateSpansInput } from "../types.js"; export const aggregateSpansTool: Tool = { name: "aggregate_spans", description: `Calculate aggregated metrics and statistics across spans. Use this tool to: - Get latency percentiles for endpoints (p50, p95, p99) - Calculate error rates by endpoint - Get request counts over time - Compare performance across environments Examples: - Endpoint latency: groupBy = ["name"], metrics = ["count", "avgDuration", "p95Duration"] - Error rates: groupBy = ["name"], metrics = ["count", "errorCount", "errorRate"] - Hourly trends: timeBucket = "hour", metrics = ["count", "errorRate"]`, inputSchema: { type: "object", properties: { observableServiceId: { type: "string", description: "Service ID to query. Required if multiple services are available.", }, where: { type: "object", description: "Filter conditions (same as query_spans)", }, groupBy: { type: "array", description: "Fields to group by", items: { type: "string", enum: ["name", "packageName", "instrumentationName", "environment", "statusCode"], }, }, metrics: { type: "array", description: "Metrics to calculate", items: { type: "string", enum: [ "count", "errorCount", "errorRate", "avgDuration", "minDuration", "maxDuration", "p50Duration", "p95Duration", "p99Duration", ], }, }, timeBucket: { type: "string", description: "Time bucket for time-series data", enum: ["hour", "day", "week"], }, orderBy: { type: "object", description: "Order results by a metric", properties: { metric: { type: "string" }, direction: { type: "string", enum: ["ASC", "DESC"] }, }, }, limit: { type: "number", description: "Maximum results to return", default: 20, }, }, required: ["metrics"], }, }; export async function handleAggregateSpans( client: TuskDriftApiClient, args: Record<string, unknown> ): Promise<{ content: Array<{ type: "text"; text: string }> }> { const input = aggregateSpansInputSchema.parse(args) as AggregateSpansInput; const result = await client.aggregateSpans(input); const header = `Aggregation Results (${result.results.length} rows):\n`; const rows = result.results .map((row, i) => { const groupStr = Object.entries(row.groupValues) .map(([k, v]) => `${k}=${v}`) .join(", "); const metrics: string[] = []; if (row.count !== undefined) metrics.push(`count: ${row.count}`); if (row.errorCount !== undefined) metrics.push(`errors: ${row.errorCount}`); if (row.errorRate !== undefined) metrics.push(`error rate: ${(row.errorRate * 100).toFixed(2)}%`); if (row.avgDuration !== undefined) metrics.push(`avg: ${row.avgDuration.toFixed(2)}ms`); if (row.minDuration !== undefined) metrics.push(`min: ${row.minDuration.toFixed(2)}ms`); if (row.maxDuration !== undefined) metrics.push(`max: ${row.maxDuration.toFixed(2)}ms`); if (row.p50Duration !== undefined) metrics.push(`p50: ${row.p50Duration.toFixed(2)}ms`); if (row.p95Duration !== undefined) metrics.push(`p95: ${row.p95Duration.toFixed(2)}ms`); if (row.p99Duration !== undefined) metrics.push(`p99: ${row.p99Duration.toFixed(2)}ms`); const timeBucketStr = row.timeBucket ? ` [${row.timeBucket}]` : ""; return `${i + 1}. ${groupStr || "(all)"}${timeBucketStr}\n ${metrics.join(" | ")}`; }) .join("\n\n"); return { content: [ { type: "text", text: header + rows, }, ], }; }