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linkedin-mcp

by senko
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Python
Remote

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    • linkedit-mcp

      SimbioLabs
      A
      license
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      quality
      D
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      Enables AI assistants to read and edit LinkedIn profiles including headline, about section, work experience, education, and skills through OAuth 2.0 authentication.
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    • LinkedIn Agent MCP

      iushv
      A
      license
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      quality
      D
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      Enables AI assistants to connect to LinkedIn, accessing profiles and companies, searching for jobs and people, managing saved jobs, updating job-search profile settings, and inspecting analytics.
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    • LinkedIn MCP Server

      himanshu31shr
      A
      license
      B
      quality
      C
      maintenance
      Enables AI agents with read/write access to LinkedIn API, including profile, posts, media, organizations, comments, reactions, and analytics.
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    • Unlinked

      larsbaunwall
      A
      license
      A
      quality
      B
      maintenance
      Connects your LinkedIn professional profile to AI assistants (Claude, GitHub Copilot) via LinkedIn's official Member Data Portability API, enabling read-only access to résumé data, activity, and recent changes.
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.8/5.0

    Scored across 9 tools

    Disambiguation4/5

    Most tools target distinct resources and actions: profile, conversations, messages, connections, posts, recent activity, and raw data domains. The only mild overlap is between get_recent_activity and list_posts/list_conversations, but the description clarifies its aggregate, consent-window scope.

    Naming Consistency5/5

    All names use consistent snake_case with clear verb_noun patterns: get_profile, list_conversations, get_conversation, search_messages, search_connections, list_posts, get_recent_activity, list_data_domains, get_data_domain. The verbs are appropriate and predictable.

    Tool Count5/5

    Nine tools is well-scoped for a LinkedIn data-access MCP server. Each tool covers a meaningful area without excessive fragmentation.

    Completeness4/5

    The surface covers core read access: profile, conversations, messages, connections, posts, recent activity, and generic raw domain retrieval. Minor gaps exist, such as no dedicated search_posts or get_post, but the generic data-domain tools provide a workaround.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources