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.11MIT
- AlicenseAqualityAmaintenanceEnables AI assistants to interact with LinkedIn by scraping profiles, companies, job postings, and getting personalized job recommendations using authenticated browser automation.1714,598 PyPI3,633Apache 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
- AlicenseAqualityCmaintenanceEnables AI assistants to access LinkedIn data through the user's logged-in browser session, supporting profiles, companies, job searches, messaging, and feed/posts.21Apache 2.0
- FlicenseCqualityCmaintenanceEnables an AI agent to drive a real LinkedIn account through a persistent browser session, covering full profile editing, people and content interactions (searching, connecting, messaging, posting, reacting), and job search with step-by-step Easy Apply submissions. It reads data through LinkedIn's internal Voyager API while writing through the actual UI, with a saved answer bank so repeated application questions are resolved automatically.39-
- AlicenseNot gradedqualityBmaintenanceEnables natural language interaction with LinkedIn, including profile retrieval, job searching, messaging, and post engagement.3Apache 2.0
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.