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LinkedIn Profile Optimizer

LinkedIn Profile Optimizer

by Ruthvik-Bandari
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

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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

    A3.5/5.0

    Scored across 6 tools

    Disambiguation4/5

    Each tool targets a recognizable scope: guidelines retrieval, single-field linting, headline review, experience review, keyword extraction, and full-profile review. The main overlap is that review_profile aggregates the other review tools and lint_field duplicates some character-limit/AI-tone checks already inside review_headline and review_experience, but the descriptions make the aggregate-vs-component relationship clear enough to select correctly.

    Naming Consistency4/5

    Five of six names follow a verb_noun pattern (get_guidelines, lint_field, review_headline, review_experience, review_profile), and the shared 'review_' prefix groups the scoring tools predictably. keyword_coverage breaks the pattern as a noun phrase, a minor deviation that is still self-explanatory.

    Tool Count5/5

    Six tools is a tight, well-scoped set for a profile-review assistant: one reference tool, three field-level checks, one keyword analysis, and one orchestrator. Every tool has a distinct job and none feels like filler.

    Completeness3/5

    The surface covers guidelines, headline, experience, keywords, and a full-profile pass, but the guidelines are section-by-section while only the Experience section gets a dedicated reviewer — About/Summary, Skills, and Education have no targeted check. lint_field partially compensates for character limits, but agents reviewing a full profile will hit a dead end on the About section, which is one of the most important LinkedIn fields.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Resources