<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/logo-dark.png"> <img alt="Scholar Feed" src="assets/logo-light.png" width="140" height="140"> </picture> </p>
Scholar Feed MCP Server
    
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
npx scholar-feed-mcp@latest initThis interactive wizard will:
- Optionally ask for an API key (or skip for anonymous access)
- Detect your MCP client (Claude Code, Cursor, or Claude Desktop)
- 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@latestEvery 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:
| Client | Config file | Notes | |--------|-------------|-------| | Cursor | .cursor/mcp.json (project) or ~/.cursor/mcp.json (global) | Restart Cursor. | | Claude Desktop | macOS: ~/Library/Application Support/Claude/claude_desktop_config.json; Windows: %APPDATA%\Claude\claude_desktop_config.json | Settings → Developer → Edit Config, then restart. | | Windsurf | ~/.codeium/windsurf/mcp_config.json | Cascade → MCP icon → Configure, then refresh. | | Cline / Roo Code | cline_mcp_settings.json | MCP 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.json | Program tab → Install → Edit mcp.json. Follows Cursor's notation. | | JetBrains (PyCharm / IntelliJ) | AI Assistant → MCP → Add → As JSON | Requires AI Assistant 2025.1+. |
A few clients need a different wrapper key or file format:
<details> <summary><strong>VS Code (GitHub Copilot), Zed, Continue, and project-scoped configs</strong></summary>
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@latestProject-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}" }
}
}
}</details>
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 (26)
Core Search & Discovery
| Tool | Description | Key Parameters | |------|-------------|----------------| | search_papers | Semantic + 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_paper | Get full paper details by arXiv ID. Also handles batch lookup and BibTeX export. | arxiv_ids, format, fields, verbose | | get_citations | Citation graph (outgoing refs or incoming citations) | arxiv_id, direction, limit, fields | | fetch_fulltext | Extract results/experiments from LaTeX source | arxiv_id |
Authors
| Tool | Description | Key Parameters | |------|-------------|----------------| | find_author | Find researchers by topic/name query, or retrieve a profile by ID. | q, id, field, limit | | co_author_graph | Co-authorship neighborhood for an author | author_ids, window_years |
Embeddings
| Tool | Description | Key Parameters | |------|-------------|----------------| | embed_text | Get 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
| Tool | Description | Key Parameters | |------|-------------|----------------| | get_field_orientation | Cheap retrieval orientation for a research area: top papers, subfields, open problems. No Pro quota. | topic, limit | | get_foundational_lineage | Foundational 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.
| Tool | Description | Key Parameters | |------|-------------|----------------| | save_paper | Bookmark a paper to your library (idempotent; feeds personalization). | arxiv_id | | unsave_paper | Remove 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_library | List your saved papers, newest first. | limit, page | | list_collections | List collections with paper counts. | (none) | | create_collection | Create a named collection (get-or-create; no error on duplicate). | name | | add_to_collection | Add a paper to a collection by name or id (also auto-saves). | arxiv_id, collection_name, collection_id | | remove_from_collection | Remove a paper from a collection (stays saved). | arxiv_id, collection_name, collection_id | | create_watch | Standing 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_watches | List watches with summary, last_evaluated_at, and pending_hits. | (none) | | check_watches | Pull new matches since the last digest (read-only, idempotent). | watch_name, watch_id, limit | | update_watch | Edit 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_watch | Dry-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_watch | Delete 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:
| Range | Meaning | Example | |-------|---------|---------| | 0.7+ | Paradigm shift or broad SOTA | New architecture that changes the field | | 0.5-0.7 | Novel method with strong results | New training technique with clear gains | | 0.3-0.5 | Incremental improvement | Applying known method to new domain | | <0.3 | Survey, dataset, or minor extension | Literature review, benchmark release |
Use novelty_min: 0.5 in search_papers to filter for genuinely novel work.
Rate Limits
| Endpoint | Limit | |----------|-------| | search_papers | 30/min | | get_paper | 30/min | | get_citations | 30/min | | fetch_fulltext | 10/min | | find_author | 20/min | | co_author_graph | 20/min | | embed_text | 30/min | | get_field_orientation | 20/min | | get_foundational_lineage | 20/min | | find_gaps | 20/min | | ask_library | 10/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
| Variable | Required | Default | Description | |----------|----------|---------|-------------| | SF_API_KEY | No | (none) | Your Scholar Feed API key (starts with sf_). Without it, runs in anonymous mode (100 calls/day). | | SF_API_BASE_URL | No | Production URL | Override 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 testsContributing
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.
