Scholar Feed

io.github.YGao2005由 GitHub 账号 YGao2005 发布v3.20.0更新于 Oct 24, 2026

Rank CS/AI/ML papers by citations, forecast impact, or code adoption; trace 23.2M citation edges.

已验证Streamable HTTP可网页运行网页搜索与抓取知识与记忆数据与分析

概览

AI 生成的概览

Scholar Feed 让助手搜索并按引用、影响力或代码采用度排序 arXiv 上的计算机与 AI/ML 论文,追溯引用关系、读取全文并管理个人文献库。

可以做什么
按关键词、语义相似度、分类、新颖度评分、趋势或引用范围搜索和发现论文,并批量查询论文详情或导出 BibTeX。向前和向后追溯引用图谱,按章节读取单篇或多篇论文全文。获取领域概览、某篇论文所在方向的基础性脉络,以及方法是否已被取代的漂移检查。保存、点赞、批注论文并归入收藏集,创建定期评估的检索监控并预览或拉取新命中。基于已保存文献库进行带引用的问答,并找出收藏集或主题中的缺口。
适用场景
适合文献综述、技术选型调研、趋势跟踪、作者发现,以及在不离开聊天客户端的情况下快速了解陌生研究领域。如果你经常处理 arXiv 论文,并希望获得排序、摘要和引用上下文,值得添加。
运行要求
可作为远程端点使用,也可通过 npx 作为本地 npm 包运行,后者需要 Node.js 18 或更高版本。匿名使用无需密钥;可选的 SF_API_KEY 来自免费账户,可提高每月额度并解锁文献库功能。需要能访问 Scholar Feed API 的网络。
安装前请注意
匿名使用按 IP 每月限 200 次调用;免费密钥为 500 次,Pro 为 10,000 次,部分工具(嵌入、缺口分析)仅限 Pro。文献库工具会写入你的账户:保存、点赞、批注、创建收藏集和监控、删除监控。SF_API_KEY 属于机密,应通过环境变量提供,不要写入共享配置。查询和论文请求会发送到第三方服务。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Scholar Feed,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.YGao2005%2Fscholar-feed-mcp/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

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

README

[Scholar Feed]

Scholar Feed MCP Server

[CI] [npm version] [Node] [License: MIT] [smithery badge]

Research paper search with ranking and citation tracking, for LLM engineering and academic research, without leaving Claude Code, Cursor, or any MCP client.

Most paper tools hand back a flat list. Scholar Feed ranks it: sort by relevance, by proven citation count, or by rising impact, then trace any paper's citation lineage forward and backward across 22M+ edges. 600k+ CS/AI/ML papers, updated daily, each with an LLM-generated summary and novelty score.

Scholar Feed indexes arXiv papers daily and ranks them on recency, citation velocity, institutional reputation, and code availability.

Quick Start

bash
npx scholar-feed-mcp@latest init

This interactive wizard will:

  1. Optionally ask for an API key (or skip for anonymous access)
  2. Detect your MCP client (Claude Code, Cursor, or Claude Desktop)
  3. Write the config and verify the connection

No API key required. Anonymous access gives you 200 calls/month, enough for a typical research session. For a higher quota (500/month per account) plus your library — collections, saved papers and watches — get a free key at scholarfeed.org/settings.

Try asking: "Search for recent papers on test-time compute scaling"

What You Can Do

Technology scouting: "What novel research on retrieval-augmented generation was published this month?"

Literature review: "Find papers similar to 2401.04088 and export their BibTeX"

Trend monitoring: "What's trending in cs.CV this week? Summarize the top 3."

Author discovery: "Who are the top researchers working on efficient LLM inference?"

Field orientation: "Give me an orientation report on sparse mixture-of-experts architectures."

Installation

The fastest path is npx scholar-feed-mcp@latest init, which auto-detects your client and writes the config. To set it up by hand, every client launches the same stdio server (npx -y scholar-feed-mcp@latest); only the config-file location and the wrapper key differ.

Claude Desktop (one-click) installs without editing any config: download the .mcpb bundle from the latest release and open it (or drag it into Settings > Extensions). The installer shows one optional field for a Scholar Feed API key (sf_...): leave it blank for anonymous mode (200 calls/month), or paste a free key from scholarfeed.org/settings for 500/month.

Claude Code takes a one-line command:

bash
# Anonymous (200 calls/month)claude mcp add scholar-feed -- npx -y scholar-feed-mcp@latest
# With an API key (500 calls/month per account)claude mcp add scholar-feed -e SF_API_KEY=sf_your_key_here -- npx -y scholar-feed-mcp@latest

Every other client takes this standard JSON block:

json
{  "mcpServers": {    "scholar-feed": {      "command": "npx",      "args": ["-y", "scholar-feed-mcp@latest"]    }  }}

To raise the quota to 500 calls/month, add "env": { "SF_API_KEY": "sf_your_key_here" } to the server entry. Get a free key at scholarfeed.org/settings.

Drop that block into the right config file:

ClientConfig fileNotes
Cursor.cursor/mcp.json (project) or ~/.cursor/mcp.json (global)Restart Cursor.
Claude DesktopmacOS: ~/Library/Application Support/Claude/claude_desktop_config.json; Windows: %APPDATA%\Claude\claude_desktop_config.jsonSettings → Developer → Edit Config, then restart.
Windsurf~/.codeium/windsurf/mcp_config.jsonCascade → MCP icon → Configure, then refresh.
Cline / Roo Codecline_mcp_settings.jsonMCP Servers sidebar icon → Configure. Cline and Roo Code share this format.
Gemini CLI~/.gemini/settings.json (or project .gemini/settings.json)
LM Studio~/.lmstudio/mcp.jsonProgram tab → Install → Edit mcp.json. Follows Cursor's notation.
JetBrains (PyCharm / IntelliJ)AI Assistant → MCP → Add → As JSONRequires AI Assistant 2025.1+.

A few clients need a different wrapper key or file format:

OpenAI Codex, VS Code (GitHub Copilot), Zed, Continue, and project-scoped configs

OpenAI Codex (~/.codex/config.toml, or $CODEX_HOME/config.toml if you set that) uses TOML, not JSON — the block above will not work. One file serves both the Codex CLI and the IDE extension.

toml
[mcp_servers.scholar-feed]command = "npx"args = ["-y", "scholar-feed-mcp@latest"]env = { SF_API_KEY = "sf_your_key_here" }

Drop the env line to run keyless at 200 calls/month. On Windows, if Codex cannot launch the server, use command = "cmd" with args = ["/c", "npx", "-y", "scholar-feed-mcp@latest"].

VS Code: GitHub Copilot (.vscode/mcp.json) uses a servers key and an explicit type, and needs Copilot agent mode. You can also run MCP: Add Server from the Command Palette.

json
{  "servers": {    "scholar-feed": {      "type": "stdio",      "command": "npx",      "args": ["-y", "scholar-feed-mcp@latest"]    }  }}

Zed (settings.json) uses a context_servers key, and the "source": "custom" line is required (without it, Zed silently skips the entry).

json
{  "context_servers": {    "scholar-feed": {      "source": "custom",      "command": "npx",      "args": ["-y", "scholar-feed-mcp@latest"]    }  }}

Continue uses YAML, with mcpServers as a list, in ~/.continue/config.yaml (global) or .continue/config.yaml (workspace).

yaml
mcpServers:  - name: scholar-feed    type: stdio    command: npx    args:      - "-y"      - scholar-feed-mcp@latest

Project-scoped (.mcp.json), to share the server across a repo:

json
{  "mcpServers": {    "scholar-feed": {      "command": "npx",      "args": ["-y", "scholar-feed-mcp@latest"],      "env": { "SF_API_KEY": "${SF_API_KEY}" }    }  }}

Windows: for any JSON config above, use "command": "cmd" and "args": ["/c", "npx", "-y", "scholar-feed-mcp@latest"].

Scholar Feed is a standard stdio MCP server, so any other MCP-compatible client works with the standard block too.

Available Tools (27)

Core Search & Discovery

ToolDescriptionKey Parameters
search_papersSemantic + keyword search with filters. Also does similar-paper discovery, citation-scoped search, and trending.q, category, novelty_min, days, sort, anchor_paper_id, scope_to_citations_of, mode, method_category, task, dataset, contribution_type, task_category, cursor, limit
get_paperGet full paper details by arXiv ID. Also handles batch lookup and BibTeX export.arxiv_ids, format, fields, verbose
get_citationsCitation graph (outgoing refs or incoming citations)arxiv_id, direction, limit, fields
fetch_fulltextRead a paper's text by section (abstract, introduction, related_work, method, results, conclusion, or all). Pass arxiv_ids to read up to 8 papers in one call; a paper that cannot be extracted comes back as a failed entry, not a failed call.arxiv_id, arxiv_ids, sections

Authors

ToolDescriptionKey Parameters
find_authorFind researchers by topic/name query, or retrieve a profile by ID.q, id, field, limit
co_author_graphCo-authorship neighborhood for an authorauthor_ids, window_years

Embeddings

ToolDescriptionKey Parameters
embed_textGet a 768-dim Gemini embedding for text (for HyDE and custom similarity). Pro-only, so anonymous/free callers get a 403 pro_required.text, task_type

Research

ToolDescriptionKey Parameters
get_field_orientationCheap retrieval orientation for a research area: top papers, subfields, open problems. No Pro quota.topic, limit
get_foundational_lineageFoundational work for a paper's niche via the citation graph (consensus-then-lift): niche_roots → field_level → discipline, with cited_by_in_niche evidence. Surfaces canonical anchors semantic search misses. No Pro quota.anchor_paper_id, scope, generality_ceiling, limit
check_drift"Is the method I use superseded — and by what?" Critique receipts + benchmark-dominance edges over ~10 LLM builder-problem families. No Pro quota.family, method, limit

Library, Collections, Watches & Gap Analysis (require SF_API_KEY)

These MUTATE or read the authenticated user's account. The core read/search tools above work anonymously; these need a key.

ToolDescriptionKey Parameters
save_paperBookmark a paper to your library (idempotent; feeds personalization).arxiv_id
unsave_paperRemove a paper from your library (idempotent).arxiv_id
like_paper"More like this" calibration signal for the For You feed (insert-only).arxiv_id
list_libraryList your saved papers, newest first (includes your notes).limit, page
annotate_paperRecord your verdict on a paper — why it matters, when to use it, why you ruled it out. Upserted; returned by list_library, so it is what a later session reads instead of re-deriving.arxiv_id, note_text, action
list_collectionsList collections with paper counts.(none)
create_collectionCreate a named collection (get-or-create; no error on duplicate).name
add_to_collectionAdd a paper to a collection by name or id (also auto-saves).arxiv_id, collection_name, collection_id
remove_from_collectionRemove a paper from a collection (stays saved).arxiv_id, collection_name, collection_id
create_watchStanding daily-evaluated saved search; get-or-create by name. Define it with a structured criteria filter (recommended) or a single seed selector.name, novelty_min, criteria, recency_days, q, collection_name, collection_id, anchor_paper_id, scope_to_citations_of, author_id, category
list_watchesList watches with summary, last_evaluated_at, and pending_hits.(none)
check_watchesPull new matches since the last digest (read-only, idempotent).watch_name, watch_id, limit
update_watchEdit a watch in place: rename, change novelty_min, or retarget its structured criteria (clears pending hits). Address by name or id.name, watch_id, new_name, novelty_min, criteria, recency_days
preview_watchDry-run a structured criteria filter over recent papers without creating a watch; returns match_count and a sample to tune before saving. Read-only.criteria, recency_days
delete_watchDelete a watch by name or id (idempotent).name, watch_id
find_gaps"What am I missing?" for a collection or topic: foundational + frontier work you haven't saved (read-only, Pro).collection_name, collection_id, topic, scope, limit
ask_library"Answer from my saved set": a cited synthesis over your library or one collection, grounded only in papers you've saved (read-only). The inverse of find_gaps. Free 20/month, then Pro 200/day.question, collection_name, collection_id, limit

Novelty Score

Every paper has an llm_novelty_score from 0.0 to 1.0:

RangeMeaningExample
0.7+Paradigm shift or broad SOTANew architecture that changes the field
0.5-0.7Novel method with strong resultsNew training technique with clear gains
0.3-0.5Incremental improvementApplying known method to new domain
<0.3Survey, dataset, or minor extensionLiterature review, benchmark release

Use novelty_min: 0.5 in search_papers to filter for genuinely novel work.

Rate Limits

EndpointLimit
search_papers30/min
get_paper30/min
get_citations30/min
fetch_fulltext (single paper)10/min
fetch_fulltext (batch, 2-8 papers)6/min
find_author20/min
co_author_graph20/min
embed_text30/min
get_field_orientation20/min
get_foundational_lineage20/min
find_gaps20/min
ask_library10/min

Responses include X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset headers.

Monthly volume quota (separate from the per-minute limits above, counted per account across all your keys): 200 calls/month anonymous (per IP), 500/month with a free key, 10,000/month on Pro. A smaller daily cap (100 / 200 / 2,000) sits underneath it as a burst guardrail so a runaway loop cannot spend a month in an hour; hitting it returns a 429 with scope: "burst" and leaves your monthly quota untouched. Read your remaining month from GET /v1/health (monthly_limit / usage_this_month) before a batch.

The AI synthesis tools have their own limits: ask_library is 20/month free, then 200/day on Pro; find_gaps is Pro-only (a 403 pro_required otherwise). embed_text needs an account of any tier — anonymous callers get a 403 account_required.

Example Response

search_papers with q: "attention mechanism" returns:

json
{  "papers": [    {      "arxiv_id": "2401.04088",      "title": "Attention Is All You Need (But Not All You Get)",      "authors": ["A. Researcher", "B. Scientist"],      "year": 2024,      "categories": ["cs.LG", "cs.AI"],      "primary_category": "cs.LG",      "arxiv_url": "https://arxiv.org/abs/2401.04088",      "has_code": true,      "github_url": "https://github.com/example/repo",      "citation_count": 42,      "rank_score": 0.73,      "llm_summary": "Proposes a sparse attention variant that reduces compute by 60% while matching dense attention accuracy on 5 benchmarks.",      "llm_novelty_score": 0.55    }  ],  "total": 1847,  "page": 1,  "limit": 20,  "next_cursor": "eyJzIjogMC43MywgImlkIjogIjI0MDEuMDQwODgifQ=="}

Pass next_cursor back to get the next page (keyset pagination, which is more stable than page numbers for large result sets).

Environment Variables

VariableRequiredDefaultDescription
SF_API_KEYNo(none)Your Scholar Feed API key (starts with sf_). Without it, runs in anonymous mode (200 calls/month).
SF_API_BASE_URLNoProduction URLOverride API base URL

Development

bash
npm installnpm run build      # Build to build/npm run dev        # Watch modenpm run typecheck  # Type check without emittingnpm test           # Run tests

Contributing

See CONTRIBUTING.md for guidelines.

Troubleshooting

"Authentication failed: your SF_API_KEY is invalid" The key may have been revoked. Generate a new one at scholarfeed.org/settings. Or remove the key to use anonymous mode.

"Rate limit exceeded" or "Anonymous daily limit exceeded" Anonymous mode allows 200 calls/month. Get a free API key at scholarfeed.org/settings for 500 calls/month per account, plus your library.

Server shows as "failed" with no error — especially right after an update The first launch (and the first launch after each new release) makes npx download the package. The published bin is a single self-contained file with no dependency tree to resolve, so this is fast — but on a slow link it can still outrun your client's start-up timeout, and the server then shows as "failed" with no detail. Fixes: (1) warm the cache by running it once in a terminal — npx -y scholar-feed-mcp@latest --version — then restart your client; (2) raise the MCP start-up timeout if your client supports it (Claude Code: MCP_TIMEOUT=60000). For the fastest, offline-capable launches, install once globally and point the config at it instead of npx:

bash
npm install -g scholar-feed-mcp# then in your MCP config:  "command": "scholar-feed-mcp", "args": []

Tool calls time out or fail silently Ensure Node.js 18+ is installed (node --version). Older versions lack the native fetch API.

Stale npx cache The config blocks above pin scholar-feed-mcp@latest, which re-resolves the newest version each launch. If you previously used an unpinned scholar-feed-mcp and are stuck on an old build: npx --yes scholar-feed-mcp@latest.

Windows: "command not found" Use "command": "cmd" with "args": ["/c", "npx", "-y", "scholar-feed-mcp@latest"] in your MCP config.

About Scholar Feed

Scholar Feed is a research-discovery engine for computer science and AI/ML papers, founded in 2025. It indexes 600,000+ papers from arXiv — ranked by novelty, citation velocity, and relevance — with LLM-generated summaries, a citation graph, author profiles, and full-text extraction. It is available as a website, a public REST API, and a Model Context Protocol (MCP) server that AI agents can call directly. This package (scholar-feed-mcp) is the open-source MCP server.

Privacy

See our privacy policy.

License

MIT

来源:README.md,提交 5ca5ede

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版本历史

1
  1. v3.20.0最新Sep 16, 2026

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