LinkedIn MCP Server
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- AlicenseNot gradedqualityCmaintenanceEnables AI agents to interact with LinkedIn for posting, commenting, liking, and managing connections via OAuth2 authentication.17 npmMIT
- AlicenseBqualityCmaintenanceEnables AI agents with read/write access to LinkedIn API, including profile, posts, media, organizations, comments, reactions, and analytics.209 npmMIT
- AlicenseBqualityBmaintenanceEnables AI agents to manage LinkedIn profiles, posts, connections, skills, education, certifications, and other professional data through the LinkedIn API.19MIT
- AlicenseBqualityCmaintenanceEnables AI agents to manage LinkedIn profiles, posts, connections, skills, education, and certifications through the LinkedIn API.18176 npm64MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to create and manage LinkedIn posts via OAuth, including text posts, URL shares, deletions, profile lookup, and company page posting.2 npmMIT
- AlicenseAqualityDmaintenanceEnables AI agents to manage professional networking on LinkedIn by providing tools for posting updates, searching for jobs, and analyzing profiles. It facilitates secure interaction with the LinkedIn platform through OAuth 2.0 authentication and the Model Context Protocol.1729 PyPI12MIT
TDQS
Scored across 34 tools
Most tools are clearly separated by resource and action, such as list_campaigns and create_comment. However, whoami overlaps with get_member_profile, and the capability/metadata tools (list_tool_catalog, list_capabilities, list_endpoint_manifest, can_execute_tool) have fuzzy boundaries that could cause an agent to pick the wrong one.
The dominant snake_case style is readable and mostly follows a verb_noun pattern like list_campaigns and create_post. But the set mixes conventions with one-word whoami, noun_verb linkedin_get/linkedin_post, and auth_* tools that resemble domain-prefixed verbs or nouns, making the pattern inconsistent.
34 tools is too many for an agent to navigate efficiently, especially with 8 auth tools, 4 capability/metadata tools, and raw HTTP helpers padding the surface. The broad LinkedIn scope justifies some size, but several utility tools could be consolidated or removed.
Core workflows for posts, comments, reactions, media upload, and ad campaigns are represented, and raw linkedin_post can fill some gaps. However, there is no update_post, no delete_comment, and no semantic tool to attach uploaded media to a post, leaving notable lifecycle dead ends.