Scholar Feed

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

• 연결 방식: 컴퓨터 또는 클라우드
• 전송 방식: stdio, Streamable HTTP
• 카테고리: 검색 및 AI 기억
• 입력해야 할 값: SF_API_KEY

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

검사 4/4 통과 · 내가 묻는 내용은 제작자 서버(mcp.scholarfeed.org)를 거쳐요

claude mcp add --transport http scholar-feed 'https://mcp.scholarfeed.org/mcp?src=mcp-registry'

{
  "mcpServers": {
    "scholar-feed": {
      "url": "https://mcp.scholarfeed.org/mcp?src=mcp-registry"
    }
  }
}

{
  "servers": {
    "scholar-feed": {
      "type": "http",
      "url": "https://mcp.scholarfeed.org/mcp?src=mcp-registry"
    }
  }
}

claude mcp add scholar-feed --env SF_API_KEY=your-value -- npx -y scholar-feed-mcp

{
  "mcpServers": {
    "scholar-feed": {
      "command": "npx",
      "args": [
        "-y",
        "scholar-feed-mcp"
      ],
      "env": {
        "SF_API_KEY": "your-value"
      }
    }
  }
}

{
  "mcpServers": {
    "scholar-feed": {
      "command": "npx",
      "args": [
        "-y",
        "scholar-feed-mcp"
      ],
      "env": {
        "SF_API_KEY": "your-value"
      }
    }
  }
}

{
  "servers": {
    "scholar-feed": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "scholar-feed-mcp"
      ],
      "env": {
        "SF_API_KEY": "your-value"
      }
    }
  }
}

어떤 앱에서 쓸 수 있나요

이 서버의 연결 방식과 각 앱의 공식 문서를 바탕으로 했어요. 2026년 11월 10일 확인.

인터넷으로 연결하는 앱

ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast

내 컴퓨터에서 실행하는 앱

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

모든 앱과 장단점 →

할 수 있는 일

도구 이름과 설명은 서버가 2026년 11월 10일에 알려 준 그대로예요. 우리가 아니라 제작자가 쓴 글이고, 일반 텍스트로만 보여 드려요.

• search_papers

  Search Scholar Feed's 600k+ CS/AI/ML paper corpus. Semantic (embedding) search by default, so it finds conceptually related work even when the wording differs. EVERY PARAMETER DOCUMENTS ITS OWN BEHAVIOUR AND COVERAGE LIMITS — read the ones you intend to use; this description cove

• get_paper

  Get full details for one or more papers by arXiv ID. Pass a single-element array for one paper; pass multiple IDs to batch-fetch up to 50 papers in one call. Pass format='bibtex' to get a .bib citation entry (bibtex is single-paper only; for multi-paper bibtex, call repeatedly).

• get_citations

  Get the citation graph for a paper, sorted by citing-paper rank_score (highest-impact first). 'citing' = outgoing references this paper cites; 'cited_by' = incoming citations from other papers. Default response is a lean 12-field shape per paper — pass verbose=true for the full 2

• fetch_fulltext

  Read arXiv papers' actual text, by section. Section selection controls context cost: ask for the one or two you need, not whole papers — 'all' is ~13.5KB a paper, 8 of them ~108KB. Section labels are inferred: check `low_confidence_sections` before calling one the paper's own. So

• find_author

  Two-mode author tool. Provide exactly one of q or id. Q-MODE (q=...): search for researchers by topic or name — uses embedding similarity for topics ('efficient LLM inference'), fuzzy matching for names ('Yann LeCun'). Returns a list of matching authors with author_id, name, h_in

• co_author_graph

  Find the co-authorship neighborhood of one or more authors. Given a list of author_ids, returns edges {from, to, papers_count, last_collab_year} where 'from' is one of the input authors and 'to' is any co-author appearing on a shared paper within the window. Use for AC reviewer t

• embed_text

  Embed a text string into a 768-dim Gemini Flash vector. Use for HyDE-style retrieval: (1) write a hypothetical short paper that would perfectly answer the user's query, (2) embed it with task_type='RETRIEVAL_DOCUMENT' (default — matches the corpus embedding side), (3) pass the re

• get_field_orientation

  Returns CANDIDATE FOUNDATIONAL PAPERS for a research topic — cheap retrieval only, no synthesis. Ranks papers by a blend of citation count (0.6 weight, captures importance) and semantic similarity to your topic (0.4 weight). Use this to bootstrap a literature survey or get a fast

• get_foundational_lineage

  Returns the FOUNDATIONAL WORK FOR A PAPER'S NICHE via the citation graph — the relative question ('what is foundational for THIS paper's specific sub-field', often itself only modestly cited) rather than the obvious global landmarks. Anchors on the paper, takes its embedding neig

• save_paper

  Save a paper to the authenticated user's Scholar Feed library (bookmark). MUTATES the library and feeds the user's personalization — saved papers are the strongest signal in the For You feed and the email digest. Idempotent: calling it again on an already-saved paper leaves it sa

• unsave_paper

  Remove a paper from the authenticated user's Scholar Feed library. MUTATES the library. Idempotent: removing a paper that isn't saved leaves it unsaved. Note: the saved library is a superset of all collections, so un-saving a paper ALSO removes it from every collection it was in.

• like_paper

  Like a paper — a 'more like this' calibration signal that tunes the user's For You feed toward similar work. INSERT-only and idempotent (liking twice is a no-op, never un-likes). Distinct from save_paper: like expresses taste for ranking; save bookmarks for later reading. Require

• list_library

  List the authenticated user's saved papers (their library), newest first. Read-only. Use this to review a reading list or to see what's already saved before saving more. Requires SF_API_KEY. SHAPE: agent callers get a lean record — `llm_summary` (~300 chars) INSTEAD of the abstra

• list_collections

  List the authenticated user's collections (named groups of saved papers) with paper counts. Read-only. Use before add_to_collection to see existing collections. Requires SF_API_KEY.

• create_collection

  Create a new named collection. MUTATES. If a collection with that name already exists, returns the existing one (get-or-create — never errors on duplicate). Use "/" to nest under a folder, e.g. "AgentOPA/Formal" — the folder is derived from the name, so there is no parent to crea

• add_to_collection

  Add a paper to a collection, addressed by collection_id OR collection_name (get-or-create by name — no need to look up an id first). Nest with "/": collection_name "AgentOPA/Formal" files the paper under an "AgentOPA" folder. MUTATES: also auto-saves the paper to the library. Ide

• remove_from_collection

  Remove a paper from a collection, addressed by collection_id OR collection_name. MUTATES (the paper stays in your library; it's only removed from this collection). Idempotent. Requires SF_API_KEY.

• create_watch

  Create a standing watch — evaluated daily against newly-indexed papers, surfacing new matches via the email digest and via check_watches. MUTATES. Get-or-create by name (re-creating with an existing name returns it unchanged — never errors on duplicate). TWO forms: (1) the v2 STR

• list_watches

  List the authenticated user's watches with name, a one-line definition summary, last_evaluated_at, and pending_hits (count of new matches since the last digest delivery). Read-only. Use before create_watch to see what's already tracked. Requires SF_API_KEY.

• check_watches

  Pull new matching papers since the last digest delivery, in the same shape as search_papers results. Optionally scope to one watch by watch_name OR watch_id; omit both for all watches. Read-only and idempotent — does NOT advance any watermark (only digest delivery does), so it is

• delete_watch

  Delete a watch, addressed by watch_id OR name. MUTATES. Idempotent: deleting a non-existent watch is a no-op (no error). To change a watch in place (rename / novelty_min / retarget criteria) use update_watch instead of delete-and-recreate. Requires SF_API_KEY.

• update_watch

  Update an existing watch in place — rename, change novelty_min, or RETARGET its structured filter `criteria`. MUTATES. Address by watch_id OR name. Changing criteria replaces the definition and clears the watch's pending hits (so stale matches don't deliver); the next daily eval

• preview_watch

  Dry-run a structured filter over recent papers WITHOUT creating a watch — the tuning loop. Returns {window_days, needs_similarity, match_count, sample} so you can iterate (add a category, raise min_novelty, switch the collection relation) before saving with create_watch. Structur

• find_gaps

  Find important work you HAVEN'T saved, for a collection or topic — a 'what am I missing?' analysis. Returns two buckets: foundational_gaps (canonical citation-graph anchors in the niche, not in your library) and frontier_gaps (recent high-novelty work in the niche, not yet saved)

• ask_library

  Answer a question using ONLY the papers you've saved — a synthesis over your library (or one collection) with inline [arXiv-ID] citations. The inverse of find_gaps (which finds important work you're MISSING): ask_library reasons over what you HAVE. Optionally scope to one collect

• annotate_paper

  Record YOUR verdict on a paper — why it matters for your work, when to use it, or why you ruled it out. One note per paper, upserted (writing again replaces it), so it is safe to call repeatedly. Requires SF_API_KEY. WHY IT MATTERS: this note is the only thing that survives betwe

• check_drift

  Answers 'for my problem, is the method I use superseded — and by what?' over a grounded, entity-resolved knowledge base of textual critique receipts + benchmark-dominance edges (no LLM call at query time). Call with a `family` (e.g. 'rag', 'peft', 'kvcache') and a `method` (e.g.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 11일 점검

• ✓ 통과

  제작자와 저장소 일치

  레지스트리 이름이 GitHub 계정 YGao2005 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

• ✓ 통과

  계속 관리되고 있음

  코드 마지막 변경: 22일 전

• … 아직 점검 안 함

  라이선스 있음

  아직 GitHub에서 이 저장소를 읽어 오지 않았어요

• ✓ 통과

  엔드포인트 응답

  123ms 만에 응답(HTTP 200)

• – 해당 없음

  OAuth로 로그인

  로그인 필요 없음

• ✓ 통과

  도구 설명에 숨겨진 지시 없음

  도구 설명 27개를 읽었고, AI에게 보내는 숨겨진 지시는 없었어요

사용자 리뷰

아직 리뷰가 없어요

Google로 로그인하고 리뷰 쓰기

검색 및 AI 기억 카테고리의 다른 서버와 비교i

• 이 카테고리의 다른 서버 5913개 중 93%보다 스타가 많아요
• 이 카테고리의 다른 서버 5913개 중 38%보다 최근에 레지스트리에 공개됐어요
• 기본 점검 결과가 이 카테고리의 다른 서버 5913개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:↓ 주간 다운로드i★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Ruflo

Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.

제작자: ruvnet·널리 사용됨·↓ 7.4만/주·★ 8.1만·어제 업데이트

내 컴퓨터에서 실행

설치

Context7

Up-to-date docs for AI agents

제작자: upstash·널리 사용됨·↓ 125만/주·★ 6.5만·그저께 업데이트

컴퓨터 또는 클라우드

설치

Metabase

Lets AI clients search, explore, query, and visualize data in a Metabase instance.

제작자: metabase·널리 사용됨·★ 5만·6개월 전 업데이트

클라우드에서 실행

설치

제작자의 README

npx scholar-feed-mcp@latest init

# Anonymous (200 calls/month)

{

[mcp_servers.scholar-feed]

{

{

mcpServers:

{