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  • F
    license
    A
    quality
    B
    maintenance
    An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.
    3
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  • A
    license
    C
    quality
    D
    maintenance
    An MCP server that provides RAG-powered Q\&A regarding Indonesia's Law No. 27 of 2022 on Personal Data Protection (UU PDP). It enables users to search for specific articles, legal definitions, data subject rights, and regulatory sanctions through Pinecone and OpenAI.
    2
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables job seekers to structure job descriptions, search a personal Markdown profile with hybrid BM25/dense retrieval, and compare a posting against that profile to see which requirements are supported, weak, or missing evidence. It also tracks applications and follow-up deadlines through JSON-backed tools whose writes stay as previews until a human turn confirms them.
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables LLM agents to search jobs, retrieve individual job details, access statistics, and mark application statuses by querying a SQLite-backed job database.
    MIT
  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables natural-language querying of structured data via Model Context Protocol, allowing AI agents to answer questions without SQL or API knowledge.
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  • F
    license
    Not graded
    quality
    D
    maintenance
    Helps users find suitable LinkedIn job opportunities by automatically scraping listings, analyzing compatibility with user profiles using AI, and sending custom match reports via email.
    1
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  • F
    license
    Not graded
    quality
    B
    maintenance
    This MCP server enables job intelligence retrieval and ranking, providing tools to list jobs, fetch details, access corpus statistics, and score candidates against job descriptions using staged retrieval to optimize LLM context usage.
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