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semantic-scholar-mcp

semantic-scholar-mcp

by ckgerteis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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    • semantic-scholar-mcp

      FujishigeTemma
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      A
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      An MCP server that provides access to Semantic Scholar's academic paper database, enabling paper search, detailed retrieval, author info, and citation export.
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    • semanticscholar-mcp

      xbghc
      A
      license
      B
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      MCP server that provides Semantic Scholar academic search capabilities, including paper search, detail query, citation analysis, author search, and intelligent recommendations.
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    • Semantic Scholar MCP Server

      truaxki
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      quality
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      Enables searching and retrieving academic papers, authors, citations, and references from Semantic Scholar via an HTTP MCP server with caching and rate limiting.
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    • semanticscholar-mcp-server

      XWang20
      A
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      An MCP server that provides access to Semantic Scholar's academic graph, recommendations, and datasets APIs, enabling paper search, citation analysis, author lookups, and dataset discovery through 20+ tools.
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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

    A3.9/5.0

    Scored across 9 tools

    Disambiguation4/5

    Most tools target clearly distinct operations: paper lookup, author lookup, search, citations, and recommendations. The main overlap is s2_recommend_single vs s2_recommend_multi, where the single-seed version is effectively a special case of the multi-seed one, which could cause misselection.

    Naming Consistency4/5

    All tools use a consistent s2_ prefix and snake_case, with mostly verb_noun naming (s2_get_author, s2_search_papers, s2_batch_papers). A few are noun-only or noun_noun (s2_citations, s2_author_papers, s2_recommend_single vs _multi), but the convention remains readable and predictable.

    Tool Count5/5

    Nine tools is well within the ideal range and each maps to a meaningful Semantic Scholar capability. No bloat or padding; the only questionable one is the redundant recommend_single, which is minor.

    Completeness4/5

    The surface covers the core read-only scholarly workflows: paper search/get/batch, author search/get/papers, citation graph traversal, and recommendations. Minor gaps exist (e.g., separate references vs citations listing, venue/field enumeration, batch author lookup), but agents can work around them for most tasks.

    Maintenance

    ActivityActive
    ResponsivenessNo recent community issues

    Resources