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Scholar Feed

ygao2005/scholar-feed-mcp
7authSTDIOregistry active
Summary

Puts arXiv's computer science archive directly into your MCP client with semantic search, citation graphs, and LLM-scored novelty rankings across 600,000 papers. The core toolkit gives you search_papers with filters for recency and contribution type, get_paper for batch lookups and BibTeX export, get_citations for traversing the reference graph, and fetch_fulltext to pull experimental results from LaTeX source. Author discovery and co-authorship graphs are built in. The field_orientation tool generates research area summaries without burning Pro credits. Ships with 100 calls per day anonymous or 1,000 with a free API key. Install is npx scholar-feed-mcp init for auto-detection or a standard stdio JSON block for any client. Built for literature reviews where you already work.

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Configuration

SF_API_KEYsecret

Optional free API key from scholarfeed.org/settings. Anonymous mode allows 100 calls/day; with a key, 1,000/day.

Categories
Search & Web Crawling
Registryactive
Packagescholar-feed-mcp
TransportSTDIO
AuthRequired
UpdatedJun 10, 2026
View on GitHub

Scholar Feed

Scholar Feed MCP Server

CI npm version Node License: MIT smithery badge

Search 600,000+ CS/AI/ML research papers with LLM-generated novelty analysis, without leaving Claude Code, Cursor, or any MCP client. Built for researchers running a literature review where they already work: search, trace citations, pull full text, and export BibTeX in the same session.

Scholar Feed indexes arXiv papers daily and ranks them using a multi-signal scoring system (recency, citation velocity, institutional reputation, code availability). Each paper has an LLM-generated summary and novelty score.

Quick Start

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 100 calls/day, enough for a typical research session. For higher limits (1,000/day per account), 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 (100 calls/day), or paste a free key from scholarfeed.org/settings for 1,000/day.

Claude Code takes a one-line command:

# Anonymous (100 calls/day)
claude mcp add scholar-feed -- npx -y scholar-feed-mcp@latest

# With an API key (1,000 calls/day 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:

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

To raise limits to 1,000 calls/day, 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:

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

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.

{
  "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).

{
  "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).

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:

{
  "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 (25)

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_fulltextExtract results/experiments from LaTeX sourcearxiv_id

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

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.limit, page
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 1/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_fulltext10/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.

Daily volume quota (separate from the per-minute limits above, counted per account across all your keys): 100 calls/day anonymous, 1,000/day with a free key, 10,000/day on Pro. The AI synthesis tools have their own limits: ask_library is 1/month free, then 200/day on Pro; find_gaps and embed_text are Pro-only (a 403 pro_required otherwise).

Example Response

search_papers with q: "attention mechanism" returns:

{
  "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 (100 calls/day).
SF_API_BASE_URLNoProduction URLOverride API base URL

Development

npm install
npm run build      # Build to build/
npm run dev        # Watch mode
npm run typecheck  # Type check without emitting
npm 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 100 calls/day. Get a free API key at scholarfeed.org/settings for 1,000 calls/day per account.

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:

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.

  • Website: https://www.scholarfeed.org
  • npm: https://www.npmjs.com/package/scholar-feed-mcp
  • REST API: https://api.scholarfeed.org/v1

Privacy

See our privacy policy.

License

MIT

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