Search LinkedIn Job Openings
/search-linkedin-job-openingsReturns up to 100 LinkedIn job openings per call, filtered by company size, salary, applicant count, recruiter presence and resume match.
Overview
search-linkedin-job-openings is the advanced job search on the SyncGTM MCP server: it returns up to 100 LinkedIn job openings per call, each with title, company, location, posted date and job URL. On top of keyword and location it filters on the company itself — headcount, founded date, follower count, and whether the poster is a recruiting agency — plus quality filters for published salary, Easy Apply, applicant count and named recruiters. It is the only SyncGTM job tool that scores openings against a resume, and at 0.3 credits a flat call it is the cheapest of the four job tools.
What it does
- 1
Takes `keyword` and `locations` as arrays, so one call can cover six job titles across four cities. Nothing is strictly required, but you must pass one of the two — a call with neither has nothing to search on.
- 2
Filters on the company, not just the job: `company_size_min` and `company_size_max`, `company_founded_date_min` and `company_founded_date_max`, `company_followers_count_min` and `company_followers_count_max`, `company_exclude`, and `exclude_recruiting_agencies` to drop agency-posted reposts.
- 3
Cuts the list to openings worth acting on with `require_salary_info`, `filter_easy_apply`, `filter_under_10_applicants` and `require_recruiter_profile`.
- 4
Scopes the time window with `published_at` — `r86400` for the past 24 hours, `r604800` for the past week, `r2592000` for the past month.
- 5
Scores each opening against a resume with `resume_keywords`, an array of `{keyword, aliases[]}` objects. No other SyncGTM job tool does this.
- 6
Returns title, company, location, posted date and job URL — up to 100 per call, deduplicated when you set `save_only_unique_items`. No emails, phones, contact names or company domains, and the quality filters gate the list rather than adding fields to it. Cost is 0.3 credits per call, flat, whatever filters you set.
How to use it
- 1
Download the skill file — it saves as search-linkedin-job-openings.zip.
- 2
In Claude, open Settings → Capabilities → Skills and upload the .zip (or unzip the search-linkedin-job-openings/ folder into .claude/skills/ for Claude Code).
- 3
Connect the SyncGTM MCP server — browser sign-in, no API key — then run `check_credits` (free) to confirm your balance before a long run of searches.
- 4
Type /search-linkedin-job-openings and describe the roles, the cities and your ICP headcount band, e.g. 'RevOps and Revenue Operations roles in London and Manchester, 50 to 500 employees, no recruiting agencies, posted in the past week, 100 results'.
- 5
Tune `max_results`. It defaults to 10 and the cost is flat at 0.3 credits, so set it to 100 on every prospecting run and pair it with `save_only_unique_items` to drop the repeats.
Use cases
ICP-sized hiring accounts
Set `company_size_min` and `company_size_max` to your segment band so the account list you hand sales matches the companies they actually close. A 40,000-person enterprise and a 12-person startup both post RevOps reqs; only one of them is your buyer.
Agency-free target lists
Set `exclude_recruiting_agencies` to true so every row is a real employer. Staffing firms repost the same req across ten clients and will fill an account list with companies that are not hiring.
Daily hiring-signal sweep
Run the same filtered search every morning with `published_at` set to `r86400` to catch reqs on the day they go live. A req you reach on day one gives you the whole hiring cycle to work the account, instead of arriving after the team is already built.
Resume-ranked shortlist
Pass `resume_keywords` with your real skills and their aliases to rank openings by how closely they match your background. Combine it with `filter_under_10_applicants` to find roles you can still be early on.
GTM workflow examples
This week's RevOps reqs → verified decision-maker list
- 1Call `search_linkedin_job_openings` with keyword ['Revenue Operations', 'RevOps', 'Sales Operations'], locations ['United States'], company_size_min 50, company_size_max 500, exclude_recruiting_agencies true, published_at 'r604800', save_only_unique_items true, max_results 100 — one call, 0.3 credits, every US mid-market RevOps opening posted in the last 7 days.
- 2Group the openings by company and keep employers with 2 or more open reqs. That is a team being built, not a backfill.
- 3Run `find_companies` with `company_names` set to that shortlist and `limit` capped to its length to resolve each one to a domain, industry and headcount — no job tool returns a domain, and this one bills 0.3 credits per company returned.
- 4Run `find_people_within_company` on each domain with job_title ['VP Sales', 'CRO', 'Head of RevOps'] — the people who signed off on the req.
- 5Run `find_work_email` on each contact's LinkedIn URL, then `verify_email` to drop undeliverable addresses before the list reaches your sequencer.
Outcome: A CSV of 30 to 60 verified decision-makers at companies that opened a RevOps req in the last 7 days, every row carrying the job title and job URL to use as the opening line of the email.
Salesforce shops adding headcount → tier-1 ABM list
- 1Call `search_linkedin_job_openings` with keyword ['Salesforce Administrator', 'Salesforce Developer', 'Revenue Systems Manager'], locations ['United Kingdom'], exclude_recruiting_agencies true, published_at 'r2592000', save_only_unique_items true, max_results 100 — companies staffing a stack they already run.
- 2Run `find_companies` with `company_names` set to the returned employers and `limit` set to the number you actually want to work, to get domains and headcount, then `find_company_techstack` (1 credit per domain) to confirm Salesforce is live rather than aspirational.
- 3Run `enrich_organization` (2 credits, domain in) on the confirmed domains for industry, size, funding and technologies, and drop anything outside the ICP band.
- 4Run `find_people_within_company` for RevOps, Sales Ops and Systems titles on the surviving accounts, then `find_work_email` on the shortlist.
Outcome: A tier-1 ABM list of UK Salesforce accounts actively adding systems headcount, each with a confirmed tech stack, firmographics and 2 to 3 named contacts with work emails.
Monday morning job-hunt shortlist
- 1Call `search_linkedin_job_openings` with keyword ['Revenue Operations Manager', 'RevOps Manager'], locations ['London'], work_type 'hybrid', experience_level 'mid-senior', published_at 'r604800', require_salary_info true, filter_under_10_applicants true, resume_keywords [{keyword: 'Salesforce', aliases: ['SFDC']}, {keyword: 'SQL', aliases: ['Postgres', 'BigQuery']}, {keyword: 'forecasting'}], max_results 100 — one call ranks the week's openings against your resume.
- 2Take the top 10 by match score and run `find_companies` with `company_names` set to those 10 and `limit` 10 to resolve them to domains — the search returns company names, not domains, and the next two tools take a domain only.
- 3Run `enrich_organization` on those domains for industry, size, funding and technologies, so you know which employers can back the band they published.
- 4Run `find_people_within_company` on each domain with job_title ['Head of RevOps', 'VP Sales'] — the hiring manager, not the recruiter.
- 5Run `find_work_email` on each hiring manager's LinkedIn URL to get a direct address instead of joining the Easy Apply queue.
Outcome: A ranked shortlist of 10 London RevOps roles posted this week, filtered to openings that publish a salary and show fewer than 10 applicants, each with a resume match score and the hiring manager's work email.
Prompts
Paste any of these into Claude once the skill is installed and the SyncGTM MCP is connected.
Search LinkedIn job openings for 'Revenue Operations', 'RevOps' and 'Sales Operations' in London and Manchester. Companies between 50 and 500 employees, exclude recruiting agencies, posted in the past week. Return 100 unique results.Search LinkedIn job openings for 'Account Executive' and 'Enterprise Account Executive' in the United States. Full-time, remote, companies under 200 employees, no recruiting agencies, posted in the past 24 hours. 100 unique results, then group them by company and show me every employer with 2 or more open reqs.Search LinkedIn job openings for 'Demand Generation Manager' and 'Growth Marketing Manager' in Berlin and Munich. Mid-senior level, only openings that publish a salary and have a named recruiter on the post. 50 results.Search LinkedIn job openings for 'Data Engineer' in New York and Austin, posted in the past month, Easy Apply only and fewer than 10 applicants. Score them against resume keywords Python (aliases PySpark, Pandas), SQL (aliases Postgres, BigQuery) and dbt, then rank by match score. 100 results.Search LinkedIn job openings for 'Customer Success Manager' and 'Head of Customer Success' in the United Kingdom. Full-time, companies founded after 2015 with 51 to 500 employees, exclude recruiting agencies. 100 unique results, then run find_companies on the top 20 company names to get their domains.Best practices
Always pass `keyword` or `locations`. Nothing on this tool is strictly required, but a call with neither has nothing to search on — every other parameter is a filter layered on top of those two.
Set `max_results` to 100 and leave `enrich_company_data` off. The cost is 0.3 credits per call flat, whatever the filters or the result count, so a 10-opening call spends the same credit as a 100-opening one. `enrich_company_data` only slows the call; when you need real firmographics, resolve domains with `find_companies` and run `enrich_organization` (2 credits, domain in).
Put every title and every city into the arrays instead of splitting the search, then set `save_only_unique_items` to true. Six titles across four cities is one call, not 24 — and a wide search returns the same opening more than once.
Set `exclude_recruiting_agencies` to true for any outbound list. Agency reposts duplicate accounts and attach the wrong company to the req, which is the fastest way to burn a sequence on a non-buyer.
If a search returns nothing, drop the quality filters first. `require_salary_info` and `filter_under_10_applicants` each exclude most openings on their own, and stacking them with a narrow headcount band empties the result set.
Pick the job tool by scope, not habit. This is the cheapest of the four at 0.3 credits; `linkedin_job_listings` (1) is the plain keyword sweep across all companies, `company_job_listings` (1.5) covers one company by name or domain, and `company_job_openings` (5) covers one company by LinkedIn URL or domain. `job_openings_growth_rate` (3) is not a search at all — it returns the hiring trend over time.