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Fresh Linkedin Profile Data MCP Server

by BACH-AI-Tools
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Python
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    TDQS

    C2.2/5.0

    Scored across 39 tools

    Disambiguation2/5

    There are multiple overlapping company and lead search tools (search_companies, get_companies, search_companies_instantly, search_companies_by_sn_url, search_leads, search_leads_v2, lead_search_at_scale), some with identical 'Get search results' descriptions. While many retrieval tools are distinct, the boundary between async search, status, and result-fetching endpoints is easy for an agent to confuse.

    Naming Consistency2/5

    The set mixes get_* and search_* patterns with exceptions like count, enrich_lead, find_custom_headcount, google_profiles, and lead_search_at_scale. It also contains inconsistent v2 suffixes, generic names, and the typo get_companys_posts, making the naming pattern unreliable.

    Tool Count2/5

    39 tools is far beyond the typical well-scoped MCP surface, even for a broad LinkedIn data product. The count feels bloated largely because search functionality is split across many overlapping variants rather than consolidated.

    Completeness4/5

    For a read-only LinkedIn data enrichment server, the surface covers companies, profiles, posts, jobs, leads, decision makers, and school/company searches quite thoroughly. Minor gaps exist, such as no explicit employee-list or full profile-experience endpoint, but most lead-generation workflows have no dead ends.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources