LinkedIn MCP Server
Related Servers
Alternatives to LinkedIn MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceControls the user's LinkedIn account through a logged-in browser session, enabling post publishing, profile viewing and editing, job searching, and Easy Apply submissions from chat.111MIT
- AlicenseAqualityBmaintenanceEnables AI assistants to interact with LinkedIn by scraping profiles, companies, job postings, and getting personalized job recommendations using authenticated browser automation.1713,039 PyPI4,163Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to manage a LinkedIn account through stealth browser automation, covering profile editing, posting, polls, inbox history, connection management, analytics, and guarded account-boundary operations.MIT
- AlicenseAqualityBmaintenanceEnables AI agents to use an existing browser session to read profiles and companies, search jobs and people, and manage messages and connection requests on LinkedIn.21Apache 2.0
- AlicenseAqualityBmaintenanceGives AI agents access to LinkedIn through your own browser session, enabling profile and company research, job searching, and management of conversations and connection requests.25Apache 2.0
- AlicenseNot gradedqualityDmaintenanceEnables Claude AI to interact with LinkedIn through browser automation, including profile reading, people and job search, company research, post publishing, and profile editing.MIT
TDQS
Scored across 101 tools
Almost every tool has a clearly distinct purpose, and the descriptions are exemplary at resolving overlaps (e.g., linkedin_get_applied_jobs vs linkedin_get_application_history, linkedin_export_connections vs linkedin_request_data_export). A few nearby pairs like linkedin_send_message vs linkedin_reply_to_conversation retain slight overlap, but misselection is unlikely given the detailed guidance.
All 101 tools follow a consistent linkedin_verb_noun snake_case pattern with a uniform prefix. Minor deviations keep it from a perfect score: profile sections use add_* while posts use create_post; edit_* and update_* are both used for modifications; and delete_*, remove_*, and withdraw_* all express removal.
At 101 tools, this is far beyond the 50+ threshold the rubric flags as an extreme mismatch. While LinkedIn's surface area is genuinely large and each tool has a defined role, the count is overwhelming for an agent to navigate effectively.
The server covers LinkedIn's core workflows end to end: profile CRUD, job search/apply/withdraw, messaging, connection management, posts, notifications, search, settings, and data export. Minor gaps like endorsements, recommendations, and Featured-section management exist, but no essential flow is left as a dead end.