An MCP server that turns your AI assistant into an academic research partner — Google Scholar search, Semantic Scholar enrichment, full-text PDF reading, citation graphs, and a local PDF corpus with hybrid retrieval.

This is an MCP server for academic literature discovery. It gives Claude (or any MCP client) 10 tools for finding, reading, and navigating papers. The primary search path goes through Google Scholar via headless Chromium — so you get the full academic web, not just what's in a single API's index — and every result is transparently enriched with Semantic Scholar metadata (stable paper IDs, full abstracts, DOIs, citation counts) so papers stay addressable across sessions.

When you find a paper worth reading, fetch_paper resolves a PDF through a four-source cascade (S2 Open Access, ArXiv, Unpaywall, publisher direct), extracts the text in memory, and returns it as context. No downloads, no local state — just the text, ready to read.

There's also an optional local corpus mode: point index_papers at a folder of PDFs and they become searchable via hybrid FAISS semantic + SQLite FTS5 keyword retrieval, fused with Reciprocal Rank Fusion and reranked by a FlashRank cross-encoder.

You ask Claude a research question; it calls VibeScholar's tools to find and read papers. Example session (illustrative):

You: Find recent papers on neural scaling laws and summarize the key results.

 ── Claude calls search_online("neural scaling laws", year_min=2023, sort="date") ──

  Found 10 papers on Google Scholar.

  1. [a1b2c3d4e5f6] "Scaling Data-Constrained Language Models" (2024)
     Muennighoff; Rush; Barak; Scao; Piktus et al.
     NeurIPS | Cited by: 189
     PDF: https://arxiv.org/pdf/2305.16264.pdf
     Snippet: We investigate scaling language models in data-constrained
     regimes ...

  2. [f7e8d9c0b1a2] "Observational Scaling Laws" (2024)
     Ruan; Maddison
     NeurIPS | Cited by: 74
     PDF: https://arxiv.org/pdf/2405.10938.pdf
     Snippet: ...

 ── Claude calls fetch_paper("a1b2c3d4e5f6") ──

  Paper: "Scaling Data-Constrained Language Models" (2024)
  Authors: Muennighoff; Rush; Barak; Scao; Piktus et al.
  Source: ArXiv open access PDF
  Pages: 1-42

  --- Page 1 ---
  Abstract. ...

Claude reads the full text and synthesizes an answer for you.

All 10 tools work this way — Claude decides when to call them based on your research question. You can ask it to search, follow citations, look up an author's publications, or read specific pages of a paper, and it picks the right tool calls.

• Google Scholar search via headless Chromium (Playwright) for full academic web coverage — not limited to a single API's index
• Semantic Scholar enrichment gives every result stable IDs, full abstracts, DOIs, and citation counts that persist across sessions
• Full-text PDF reading through a four-source resolution cascade (S2 Open Access → ArXiv → Unpaywall → publisher direct), extracted in memory and returned as context
• Citation tracking — find papers that cite a given work, or discover related papers through Scholar's "Related articles"
• Author profiles — list a researcher's publications from their Google Scholar profile
• Local PDF corpus with hybrid FAISS semantic + FTS5 keyword search, Reciprocal Rank Fusion, and FlashRank cross-encoder reranking
• Remote access via HTTP transport with Tailscale Funnel support for serving the MCP to Claude on other machines

Requires Python 3.11+.

pip install -e .
playwright install chromium

Local (stdio transport, for Claude Code):

{
  "mcpServers": {
    "vibescholar": {
      "command": "python",
      "args": ["/path/to/server.py"],
      "env": {
        "VIBESCHOLAR_DATA_DIR": "/path/to/data"
      }
    }
  }
}

Remote (HTTP transport, for access from other machines):

python server.py --transport streamable-http --host 127.0.0.1 --port 8765

Then expose via a reverse proxy or Tailscale Funnel. The server runs in stateless mode when using HTTP transport so it works behind proxies without session affinity. See REMOTE-SETUP.md for a full walkthrough.

{
  "mcpServers": {
    "vibescholar": {
      "type": "url",
      "url": "https://your-host/mcp"
    }
  }
}

Google Scholar results are enriched with Semantic Scholar metadata (stable IDs, full abstracts, DOIs) so papers remain accessible across sessions.

For searching a personally-indexed collection of PDFs. Index a folder once, then search it with hybrid semantic + keyword retrieval and cross-encoder reranking.

All search tools accept a detail parameter: "brief" for compact output (titles, authors, scores) or "detailed" for full output with abstracts or text snippets.

Online:   Query → Google Scholar (headless Chromium) → S2 enrichment → Results
                                                                     ↘ PDF cascade → In-memory text extraction

Local:    Query → FAISS (semantic, HNSW) ─┐
                                          ├→ RRF fusion → Cross-encoder rerank → Results
          Query → FTS5  (keyword)  ───────┘

Stack: FastEmbed (BAAI/bge-small-en-v1.5, 384-dim ONNX) · FAISS HNSW · SQLite FTS5 · FlashRank cross-encoder · Google Scholar (Playwright) · Semantic Scholar · Unpaywall · FastMCP

Data directory layout:

~/.vibescholar/
  index.sqlite3      # SQLite database with FTS5
  vectors.faiss       # FAISS HNSW index
  vectors.deleted.npy # Soft-deleted vector IDs (if any)
  model_cache/        # FastEmbed + FlashRank model files

• Claude Code or any MCP-compatible client
• Python 3.11+
• Playwright with Chromium (playwright install chromium)
• Optional: Semantic Scholar API key for higher rate limits

• Online papers are ephemeral. fetch_paper extracts text in memory and discards the PDF. Nothing is written to the database or FAISS index. Use save_paper if you want a local copy, or index_papers to make a folder of PDFs searchable.
• Google Scholar scraping is rate-limited. Playwright drives a real Chromium instance to avoid trivial blocking, but Scholar will throttle aggressive use. This is designed for conversational research, not bulk harvesting.
• Not a citation manager. VibeScholar finds and reads papers — it doesn't manage bibliographies, export BibTeX, or track your reading list.

pip install -e ".[test]"

# Fast unit tests (~6s)
python -m pytest tests/ -v --ignore=tests/test_index_and_retrieval.py

# Full suite including integration tests (~2 min)
python -m pytest tests/ -v

Issues and PRs welcome — see CONTRIBUTING.md.

MIT