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@postman/postman-mcp-server

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Postman MCP Server — connect AI agents (Claude Code, Cursor, VS Code Copilot, Gemini CLI) to your Postman collections, specifications, and environments via Model Context Protocol (MCP)

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import { z } from 'zod'; import { asMcpError, McpError, defineToolAnnotations } from './utils/toolHelpers.js'; export const method = 'listRunsForExecution'; export const title = 'List runs for a monitor execution'; export const description = 'Lists runs for a monitor execution. Each execution may produce multiple runs across regions. Returns run metadata including region, state, result counts, and timestamps. Not paginated.\n\nThis is Step 2 of the monitor-run workflow: listMonitorExecutions → listRunsForExecution → getMonitorRunResults. Pass the executionId from listMonitorExecutions. Returns run objects whose `id` is the runId needed by getMonitorRunResults.'; export const parameters = z.object({ monitorId: z .string() .describe("The monitor's ID. Must be a plain UUID (e.g. `1f00bf12-7ee5-4500-a1a7-3c721a03d42c`), not a uid with numeric prefix."), executionId: z.string().describe("The execution's ID, obtained from listMonitorExecutions."), }); export const annotations = defineToolAnnotations({ title: 'List runs for a monitor execution', readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: false, }); export async function handler(args, extra) { try { const endpoint = `/monitors/${encodeURIComponent(String(args.monitorId))}/executions/${encodeURIComponent(String(args.executionId))}/runs`; const result = await extra.client.get(endpoint, { headers: extra.headers }); return { content: [ { type: 'text', text: typeof result === 'string' ? result : JSON.stringify(result, null, 2), }, ], }; } catch (e) { if (e instanceof McpError) { throw e; } throw asMcpError(e); } }