
Naukri Jobs Scraper (Apify) - Naukri.com jobs with salary in LPA
io.github.datadmon由 GitHub 账号 datadmon 发布v1.0.1更新于 Nov 6, 2026
Naukri jobs as JSON: salary in LPA, skills, applicants, full JD, whole-board daily feed. $0.80/1k
发布方自己的服务:Apify MCP Server
概览
托管在 Apify 上的 MCP 服务,接入 Naukri Jobs Scraper,让助手获取 Naukri.com 职位信息,含以 LPA 计的薪资、技能、申请人数和完整描述。
- 可以做什么
- 通过 Apify 运行 Naukri.com 职位抓取,取回 JSON 或数据集形式的职位记录。
- 获取以 LPA 计的薪资、经验、技能、申请人数与浏览量、AmbitionBox 评分及完整职位描述。
- 导出整站每日职位流,或使用仅新增模式让每条职位只返回一次。
- 可通过 Apify 的同步运行接口取结果,或对长时间运行轮询后读取数据集。
- 适用场景
- 适合需要让助手检索或持续监控 Naukri.com 招聘信息,用于招聘调研、薪资对标或每日职位推送,而不想自己编写抓取代码的场景。
- 运行要求
- 远程 MCP 端点 mcp.apify.com,需要 Apify 账号。认证方式为 OAuth,或在 Authorization 请求头中携带 Bearer 形式的 Apify 令牌。运行费用计入你自己的 Apify 账号。
安装
在 SourceWeft 中
- 打开 控制台中的 Naukri Jobs Scraper (Apify) - Naukri.com jobs with salary in LPA,将其添加到工作区。
- 连接 所需的 凭据。它们会被私密存储,绝不会在公开页面上展示。
- 为需要使用其工具的对话启用该服务。
Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。
交给智能体安装
把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。
阅读 https://sourceweft.com/mcp/io.github.datadmon%2Fnaukri-jobs-scraper/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。其他 MCP 客户端
把它添加到你客户端的 mcpServers 配置中。 客户端会在首次连接时完成 OAuth 登录。
{
"mcpServers": {
"naukri-jobs-scraper-apify-naukri-com-job": {
"type": "http",
"url": "https://mcp.apify.com/?tools=data_daemon/naukri-jobs-scraper"
}
}
}README
Daemon Data - Apify actor examples
Ready-to-run Python, Node.js, curl and n8n examples for the Daemon Data pay-per-result data actors on Apify, plus an AGENTS.md and a Claude/agent skill that describe every actor (id, when to use it, input, output fields) for AI agents and coding assistants.
Every actor reads a site's own JSON endpoints, so runs take seconds, need no proxy or login, and you pay per record returned (compute is included).
India Jobs MCP client setup
India Jobs MCP gives agents five tools across ten Indian boards. Use the tested Claude Desktop/Code, Cursor and Gemini CLI configs, including Bearer-header setup and a discovery-only connection check. Board searches charge your own Apify account.
Actors
Guides and a page per API with live prices: https://datadmon.github.io/ (machine-readable index: https://datadmon.github.io/llms.txt).
The hex id and data_daemon/<name> are interchangeable everywhere in the Apify API (write the name as data_daemon~<name> inside URLs).
Ready-to-fork templates (GitHub Actions)
Complete tools you copy with Use this template, add an APIFY_TOKEN secret, and leave running:
Quick start (SDK)
REST: one call in, data out
run-sync-get-dataset-items starts the actor, waits (up to 300 s) and returns the dataset items in the response. No SDK needed.
- Add
&format=csv&fields=title,company.name,locations,applicants,postedAt,urlfor a CSV with chosen columns (alsoxlsx,xml,rss). - For runs that can exceed 300 s (full-board exports, thousands of Oracle requisitions) start the run with
POST /v2/acts/{id}/runs, pollGET /v2/actor-runs/{runId}, then readGET /v2/datasets/{datasetId}/items. - Working versions of both patterns, for every actor:
examples/run-sync/curl.sh,run_sync.py(stdlib only),run_sync.mjs(Node 18+, no dependencies).
Schedule it: only new jobs, every morning
examples/only-new-jobs/ is a daily loop for IIMJobs, Hirist, Foundit and Instahyre that returns each job once. It reads the actor's live input schema and uses onlyNewSinceLastRun (actor-side memory in your own Apify account, skipped jobs are not charged) or skipJobIds when the build offers them, and always keeps a local seen-ids file as a safety net. Python and Node versions, cron and GitHub Actions snippets included.
No-code alternative: in Apify Console, fill the input, Save as task, Schedules → Create schedule (daily), then in the task's Integrations tab add Google Sheets, Slack, a webhook, Make or Zapier.
n8n
n8n/iimjobs-new-jobs-to-google-sheets-and-slack.json: Schedule (08:00 daily) → HTTP Request torun-sync-get-dataset-items→ Google Sheets append or update (matched on jobid, so re-runs never duplicate rows) → one Slack message with the count. Uses only built-in n8n nodes.n8n/<actor>.json: the same schedule → HTTP Request → Google Sheets append pattern for every actor.
Import with Workflows → Import from file, then create two credentials: Header Auth (name Authorization, value Bearer <your Apify token>) for the HTTP Request node, and your Google Sheets (and Slack) account. Replace the sheet URL in the Google Sheets node.
AI agents and MCP
AGENTS.md: every actor's id, when to use it (and when not to), a verified input example, output fields and list price, plus the REST/MCP/CLI call patterns and cost-sizing rules. Drop it into your agent's context or repository.skills/daemon-data-apify-actors/SKILL.md: the same catalog as a skill with frontmatter, for Claude Code, Codex and other skill-aware assistants.- MCP: add
https://mcp.apify.com?tools=data_daemon/iimjobs-jobs-scraper,data_daemon/india-ipo-tracker(any list of our actor names) to your MCP client, with headerAuthorization: Bearer <APIFY_TOKEN>or OAuth. Docs: https://docs.apify.com/platform/integrations/mcp
Pricing (list prices, October 2026)
Pay per event; platform compute is included. The actor page shows the price for your Apify plan.
Apify's free plan includes $5 of usage a month, enough to try every example here. Cap test runs with maxJobs / maxItems.
Support
Bug or missing field? Open an issue on the actor's Issues tab on Apify (fastest) or here. A quick review on the actor page helps other users find it.
MIT licensed. Not affiliated with IIMJobs, Hirist, foundit, Instahyre, Oracle, Tickertape, Chittorgarh, InvestorGain, the Dubai Land Department, The Gazette or Luma.
来源:README.md,提交 ed40d9b
工具
0版本历史
1- v1.0.1最新Nov 6, 2026
评分
已满足适用检查项的 75%
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