Scholar Feed Server

Search 560,000+ CS/AI/ML research papers with LLM-powered novelty analysis from Claude Code, Cursor, or any MCP client.

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 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 (500/day), 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."

Deep dives — "Run a deep research session on 'reasoning in large language models'"

Benchmark tracking — "Show me the MMLU leaderboard and compare GPT-4 vs LLaMA-3"

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

Manual Installation

Claude Code

# Without API key (anonymous, 100 calls/day)
claude mcp add scholar-feed -- npx -y scholar-feed-mcp

# With API key (500 calls/day)
claude mcp add scholar-feed -e SF_API_KEY=sf_your_key_here -- npx -y scholar-feed-mcp

Cursor (.cursor/mcp.json)

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

To add an API key, add "env": { "SF_API_KEY": "sf_your_key_here" } to the config.

Claude Desktop (claude_desktop_config.json)

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

Project-scoped (.mcp.json)

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

Windows note: Use "command": "cmd" and "args": ["/c", "npx", "-y", "scholar-feed-mcp"].

Available Tools (23)

Core Search & Discovery

Tool	Description	Key Parameters
search_papers	Full-text keyword search with filters	q, category, novelty_min, days, method_category, task, dataset, contribution_type, task_category, has_results, cursor, limit
get_paper	Get full paper details by arXiv ID	arxiv_id, fields
find_similar	Find similar papers via embedding + bibliographic coupling	arxiv_id, limit, days
get_citations	Citation graph (outgoing refs or incoming citations)	arxiv_id, direction, limit, fields
whats_trending	Today's trending papers by composite score	category, limit, fields, exclude_ids
batch_lookup	Look up multiple papers at once	arxiv_ids (max 50), fields

Paper Content

Tool	Description	Key Parameters
fetch_fulltext	Extract results/experiments from LaTeX source	arxiv_id
fetch_repo	Get GitHub repo README + file tree	arxiv_id
export_bibtex	Export BibTeX for papers	arxiv_ids (max 50)
get_paper_results	Structured benchmark results from a paper	arxiv_id

Benchmarks & Methods

Tool	Description	Key Parameters
search_benchmarks	Find datasets/benchmarks by name	q, limit
get_leaderboard	SOTA leaderboard for a dataset	dataset, metric, limit
get_benchmark_stats	Score distribution stats (min, max, median, etc.)	dataset, metric
get_benchmark_timeline	Raw score data points over time	dataset, metric
search_by_method	Search by technique name (LoRA, YOLO, DPO, etc.)	q, contribution_type, task_category, limit
compare_methods	Side-by-side model comparison across benchmarks	models (2-10), dataset, metric

Authors

Tool	Description	Key Parameters
discover_authors	Find researchers by topic or name	q, field, limit
get_author	Detailed author profile (h-index, topics, top papers)	author_id
get_author_papers	All papers by an author (paginated)	author_id, limit, page

Research

Tool	Description	Key Parameters
get_research_landscape	Aggregated landscape stats for a topic	q, limit
deep_research	Multi-round research synthesis (30-120s)	topic, depth
refine_research	Follow-up question on a completed research report	report_id, question, date_from, date_to

Utility

Tool	Description	Key Parameters
check_connection	Verify API key, show plan and usage	—

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
check_connection	60/min
search_papers	30/min
get_paper	60/min
find_similar	20/min
get_citations	30/min
whats_trending	30/min
fetch_fulltext	10/min
batch_lookup	20/min
fetch_repo	10/min
export_bibtex	20/min
deep_research	5/min
refine_research	5/min
search_benchmarks	30/min
get_leaderboard	30/min
get_benchmark_stats	30/min
get_benchmark_timeline	30/min
search_by_method	30/min
compare_methods	20/min
discover_authors	20/min
get_author	60/min
get_author_papers	30/min
get_research_landscape	10/min
get_paper_results	30/min

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

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 — more stable than page numbers for large result sets).

Verify Installation

After setup, ask your AI assistant to run check_connection. You should see:

{
  "status": "ok",
  "plan": "free",
  "key_name": "my-key",
  "usage_today": 0
}

Environment Variables

Variable	Required	Default	Description
SF_API_KEY	No	—	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 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 500 calls/day.

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 If you're stuck on an old version after an update: npx --yes scholar-feed-mcp@latest

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

License

MIT