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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 } from './utils/toolHelpers.js'; export const method = 'getContextGraphAsk'; export const title = 'Get a Context Graph ask'; export const description = "Gets a submitted Context Graph ask's status and, once it finishes, its result. Call\nthis after submitContextGraphAsk with the \\`askId\\` it returned, and poll while\n\\`status\\` is \\`pending\\` or \\`running\\` — leave a few seconds between polls rather than\ncalling in a tight loop.\n\\`result\\` is only present once \\`status\\` is \\`completed\\`; a \\`failed\\` ask carries a short\n\\`error\\` instead. When reporting a completed ask, treat \\`result.answer\\` as the prose\nsummary and \\`result.structured\\`, \\`result.citations\\`, and \\`result.provenance\\` as the\ngraph data it rests on — cite that evidence rather than presenting the answer on its\nown, and say so when \\`result.provenance.truncated\\` is \\`true\\`, since the investigation\nwas cut off before it finished and the answer may be partial.\n"; export const parameters = z.object({ askId: z.string().describe("The ask's ID.") }); export const annotations = { title: 'Get a Context Graph ask', readOnlyHint: true, openWorldHint: false, destructiveHint: false, idempotentHint: true, }; export async function handler(args, extra) { try { const endpoint = `/context-graph/asks/${encodeURIComponent(String(args.askId))}`; const query = new URLSearchParams(); const url = query.toString() ? `${endpoint}?${query.toString()}` : endpoint; const options = { headers: extra.headers, }; const result = await extra.client.get(url, options); return { content: [ { type: 'text', text: `${typeof result === 'string' ? result : JSON.stringify(result, null, 2)}`, }, ], }; } catch (e) { if (e instanceof McpError) { throw e; } throw asMcpError(e); } }