jobdrop 2.1.1

20-source job scraper library + MCP server. LinkedIn, Indeed, Glassdoor, Google, ZipRecruiter, Wellfound, Hiring Cafe, Greenhouse, USAJobs, Adzuna, Jooble, Findwork, The Muse, Insight Global, Clearance Jobs, Kforce, CollabWork, Naukri, Bayt, BDJobs.

jobdrop

A multi-source job scraper. Hits 20 job boards in one call, normalizes the results into a pandas DataFrame, and ships with anti-bot handling for the boards that block standard scrapers.

Maintainer: this project is maintained by kbwhodat. Substantially extended from the original cullenwatson/JobSpy (MIT licensed) with new sources, an integrated MCP server, salary/seniority filters, and reliability fixes across all scrapers.

What's in here

20 sources

site_name	Source	Notes
linkedin	LinkedIn	Public listings + optional detail-page enrichment
indeed	Indeed	GraphQL with per-company cap + paginate-until-quota
glassdoor	Glassdoor	Listings + company reviews + salary data
google	Google Jobs	SERP aggregation across many sources
zip_recruiter	ZipRecruiter	US/Canada-focused
hiring_cafe	Hiring Cafe	AI-curated, ~140 jobs/page with rich tags (seniority, comp, skills, workplace_type)
wellfound	Wellfound (formerly AngelList)	50k+ startup roles
collab_work	CollabWork	Community/newsletter aggregator (~2k curated roles, fastest source)
greenhouse	Greenhouse-hosted boards	Most YC and Series A+ companies; 3-layer staleness filter
bayt	Bayt	Middle East focused
naukri	Naukri	India's largest job portal
bdjobs	BDJobs	Bangladesh's premier job portal
usajobs	USAJobs.gov	US federal public API
adzuna	Adzuna	Public API, 100% salary fill rate
jooble	Jooble	Public API, 60+ countries
findwork	Findwork.dev	Developer-focused public API
the_muse	The Muse	Culture-forward public API
insight_global	Insight Global staffing	Server-rendered listings
clearance_jobs	ClearanceJobs (DHI)	Security-cleared roles, full JD + salary + structured job_type
kforce	Kforce staffing	Direct backend API for fast results

Quality + reliability tightening

• LinkedIn — salary extraction from description body, optional per-company cap, parallel detail fetches.
• Indeed — radius GraphQL fix, per-company cap to surface diverse employers, pagination loop hardened.
• ClearanceJobs — parallel detail-page fetch so you get full JD, salary range, structured job_type, authoritative remote bool (vs the API's 200-char preview alone).
• Greenhouse — three layers of stale-protection (404 drop / past application deadline / first-published age with a 90-day default that respects hours_old).
• Wellfound + Hiring Cafe — added with anti-bot handling that defeats the strictest CDN/WAF tiers in the catalog.

Bundled credentials

API keys for USAJobs, Adzuna, Jooble, Findwork, and The Muse are baked into a positional resolver (jobdrop/_defaults.py) so the new sources work without environment setup. User-set env vars still win via setdefault semantics.

Installation

As a Python library

pip install -U jobdrop

Python ≥ 3.10 required.

As an MCP server (Claude Desktop / Claude Code / Cursor / Cline)

Install the binary once with uv tool install (or pipx install):

uv tool install "jobdrop[mcp]"
# or:  pipx install "jobdrop[mcp]"

Then add to your MCP client config — e.g. ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "jobdrop": {
      "command": "jobdrop-mcp-server"
    }
  }
}

That's it — the client launches jobdrop-mcp-server as a stdio subprocess on demand. No daemon, no port, no nix.

Note: prefer the uv tool install path so the binary lands in PATH and the client launches it directly — same pattern as reference MCP servers (filesystem, git, etc.).

Usage

from jobdrop import scrape_jobs

jobs = scrape_jobs(
    site_name=["insight_global", "clearance_jobs", "kforce", "greenhouse",
               "linkedin", "indeed", "google"],
    search_term="site reliability engineer",
    location="Atlanta, GA",
    results_wanted=20,
    hours_old=720,          # 30-day freshness cap
    country_indeed="usa",
)
print(f"Found {len(jobs)} jobs")
print(jobs[["site", "title", "company", "location", "min_amount", "max_amount", "job_url"]].head())

Parameters

scrape_jobs(
  site_name              list[str] | str — any of the 17 sources above (default: all)
  search_term            str        — keyword query
  google_search_term     str        — Google Jobs override (only filter for `google`)
  location               str        — "City, ST" or ZIP. Each scraper geocodes its own way.
  distance               int        — radius miles, default 50
  is_remote              bool       — remote-only filter (where supported)
  job_type               str        — "fulltime" | "parttime" | "contract" | "internship"
  easy_apply             bool       — direct-board apply only (LinkedIn easy-apply is broken)
  results_wanted         int        — per-site target
  offset                 int        — pagination offset
  hours_old              int        — drop postings older than N hours
  country_indeed         str        — Indeed/Glassdoor country (see list below)
  description_format     str        — "markdown" | "html"
  enforce_annual_salary  bool       — convert hourly/monthly to yearly
  linkedin_fetch_description  bool  — full JD + direct URL (slower)
  linkedin_company_ids   list[int]  — filter LinkedIn by company IDs
  proxies                list[str]  — round-robin proxies, "user:pass@host:port"
  ca_cert                str        — CA cert path for proxies
  user_agent             str        — override the default UA
  verbose                int        — 0 errors / 1 warnings / 2 all
)

Per-scraper limitations

• Indeed — only one of hours_old / (job_type+is_remote) / easy_apply per call.
• LinkedIn — only one of hours_old / easy_apply per call.
• ClearanceJobs — location/remote filters require facet IDs from the dropdown endpoints (not implemented). Filter client-side or scope by keyword.
• InsightGlobal — does not expose client-company name (it's the staffing firm). is_remote is not available in their data.
• Greenhouse — Google indexes some postings after they're filled. Stale 404s are filtered out; the freshness cutoff filters "live but ancient" postings (default 90 days, override with hours_old).

JobPost schema

JobPost
├── id, title, company_name, company_url, job_url
├── location { country, city, state }
├── description
├── is_remote
├── date_posted
├── job_type        fulltime | parttime | contract | internship
├── compensation
│   ├── interval   yearly | monthly | weekly | daily | hourly
│   ├── min_amount, max_amount, currency
│   └── salary_source
├── job_level                                  (LinkedIn, ClearanceJobs)
├── company_industry                           (LinkedIn, Indeed, Greenhouse, Kforce)
├── company_country, company_addresses,
│   company_employees_label, company_revenue_label,
│   company_description, company_logo          (Indeed)
├── skills, experience_range,
│   company_rating, company_reviews_count,
│   vacancy_count, work_from_home_type         (Naukri)
└── emails

Indeed / Glassdoor country list

Pass country_indeed (use the exact name; * = also supported on Glassdoor):

Argentina	Australia*	Austria*	Bahrain
Belgium*	Brazil*	Canada*	Chile
China	Colombia	Costa Rica	Czech Republic
Denmark	Ecuador	Egypt	Finland
France*	Germany*	Greece	Hong Kong*
Hungary	India*	Indonesia	Ireland*
Israel	Italy*	Japan	Kuwait
Luxembourg	Malaysia	Mexico*	Morocco
Netherlands*	New Zealand*	Nigeria	Norway
Oman	Pakistan	Panama	Peru
Philippines	Poland	Portugal	Qatar
Romania	Saudi Arabia	Singapore*	South Africa
South Korea	Spain*	Sweden	Switzerland*
Taiwan	Thailand	Turkey	Ukraine
United Arab Emirates	UK*	USA*	Uruguay
Venezuela	Vietnam*

LinkedIn searches globally and uses only location. ZipRecruiter is US/Canada and uses only location. Bayt searches internationally with only search_term.

Notes

• Most boards cap a single search at ~1000 results.
• LinkedIn rate-limits aggressively around the 10th page of pagination on a single IP. Use proxies.
• For Indeed search-term tuning: it searches the description too. Use -foo to exclude, "exact phrase" for exact match. Example:

  search_term='"site reliability engineer" (kubernetes OR terraform) -recruiter'
  

• For Google: copy the exact filter syntax from a real Google Jobs search and pass it as google_search_term.
• For Greenhouse: keyword + location are passed straight to a Google site:greenhouse.io query, so Boolean operators and quotes work. Don't quote the full "City, ST" — quote the city alone, leave the state bare.

License

MIT. See LICENSE.