linkedin-applicants-mcp
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- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to automate recruiter-side candidate sourcing by batch-searching, capturing resume screenshots and share links, scoring and filtering profiles, exporting reports, and sending greetings through a logged-in Chrome session.-
- AlicenseNot gradedqualityBmaintenanceLinkedIn from the user's own logged-in Chrome session for AI agents: search, profiles, post engagers, company employees, lists, sequences, inbox and a Research Pack, with hard caps and a human approval queue. Local-first, no headless browser.13MIT
- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to automate recruiter-side candidate sourcing by searching multiple keywords, capturing resume screenshots, extracting share links, deduplicating results, scoring candidates, exporting reports, and sending greetings through a logged-in Chrome session.-
- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to automate recruiter-side candidate sourcing and outreach by searching with multiple keywords, viewing and screenshotting resumes to extract share links, maintaining a persistent candidate database, filtering and scoring candidates, exporting Markdown reports, and sending greetings through a logged-in Chrome session.-
- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to automate recruiter workflows by batch-searching candidates, capturing and OCR-ing resume screenshots, scoring and filtering applicants, extracting share links, greeting candidates, and exporting Markdown reports.-
- AlicenseNot gradedqualityDmaintenanceEnables comprehensive LinkedIn profile search, data extraction, and contact enrichment using Google Search, Apollo.io, and AI-powered analysis. Includes automated data mining workflows with database storage and CSV export capabilities.3ISC
TDQS
Scored across 27 tools
Tools are grouped by clear resource prefixes (browser_, debug_, jobs_, applicants_, queue_, pacing_) and the descriptions sharply separate crawling stages—list sync vs detail fetch vs profile fetch. A couple of controls could be confused (queue_start vs queue_resume, debug_click vs debug_navigate), but the descriptions resolve those boundaries.
Most names follow a domain_prefix + verb pattern (queue_pause, pacing_set, applicants_export), so the set is predictable. Deviations include 'browser_open_login' (resource+verb+object), noun-only names like 'resume_text' and 'queue_tasks', and mixed applicant/applicants singular/plural, but these are minor.
At 27 tools, this exceeds a comfortable agent surface and falls in the 'too many' band, especially since 6 debug_* and 9 queue_* tools are operational rather than core applicant functions. The browser-automation domain justifies some of them, but an agent will still face a large selection problem.
The core read/extract lifecycle is well covered: jobs sync/list, applicant list/detail/profile/resume fetch, local search/get, and export. Gaps are mostly write-side (no shortlist, rate, or status changes) and there is no one-shot 'sync everything', but those appear to be out of scope or handled via manual browser work.