Scholar MCP

Find the paper. Follow the evidence. Build the field.

Documentation · English · 简体中文

Scholar MCP gives your AI agent a research library. It searches scholarly sources in parallel, with optional keys for additional indexes, follows citations and related work, reads the original paper down to its figures and tables, and keeps what matters in a local library with your notes. It works in Claude, Codex, Cursor, VS Code, Gemini CLI, OpenCode and any other MCP client, and from the command line or Python. Free for research and education, with commercial licenses for companies.

📰 News

• November 2026, 0.10.0. Searches no longer wait on a source whose connection hangs, OpenAlex's daily allowance lasts about four times as long, and Scholar is free for research and education under PolyForm Noncommercial, with commercial licenses for companies.
• November 2026, 0.9.0. scholar-mcp install sets Scholar up in 32 agents, one key file serves all of them, a slow source no longer holds up the others, and scholar-mcp doctor --network checks every link.
• October 2026, 0.8.5. Google Scholar sessions recover on their own, and arXiv falls back to its own search when its API is down.

🚀 Get started

1. Install uv if you do not have it, then install Scholar:

   uv tool install --python 3.12 --find-links https://github.com/Liyux3/scholar-mcp/releases/latest/download/wheel-index.html "scholar-mcp[rerank]"

1. Connect it to your agents. Scholar finds the clients on your machine and registers itself, with a backup of every file it touches:

   scholar-mcp install --all

In a client the installer does not know, ask your agent to install Scholar by following llms-install.md. Name clients to choose (scholar-mcp install claude-code codex), add --dry-run to preview the change, or --profile research for the citation graph and paper library tools. scholar-mcp update upgrades the command every connected client runs, and uvx scholar-mcp@latest install CLIENT refreshes a client pinned to a uvx version.

1. Restart the client and ask a research question, for example *find recent work on evaluating long-term memory in LLM agents, read the most cited one and compare its benchmarks*.

Other ways to install

Any stdio MCP client can launch Scholar directly through uvx, with nothing installed in advance:

{

"mcpServers": { "scholar": { "command": "uvx", "args": [ "--python", "3.12", "--find-links", "https://github.com/Liyux3/scholar-mcp/releases/latest/download/wheel-index.html", "--from", "scholar-mcp[rerank]", "scholar-mcp" ] } } }

For Claude Code the same command is claude mcp add scholar -- uvx --python 3.12 --find-links --from 'scholar-mcp[rerank]' scholar-mcp.

The repository is also a plugin marketplace for Codex and Claude Code, bundling the research tools and the Deep Research skill:

codex plugin marketplace add Liyux3/scholar-mcp && codex plugin add scholar-mcp@scholar-mcp

claude plugin marketplace add Liyux3/scholar-mcp && claude plugin install scholar-mcp@scholar-mcp

Releases also ship macOS desktop bundles (MCPB) with Python included, a multi-architecture container at ghcr.io/liyux3/scholar-mcp, and the MCP Registry entry. See the distribution matrix and llms-install.md for agents installing it themselves.

🧰 What your agent can do

Every identifier Scholar returns, a paper URL, or an exact title can be passed back to any tool. Responses carry readable YAML for the model and structured data for the client; scholar://status reports the sources and reranker in use.

🧭 How it works

Your question goes to every source at once, in the form each one searches best, and no source holds up the others. Records from OpenAlex, Semantic Scholar, arXiv, Crossref, OpenReview, PubMed, Europe PMC, DBLP, zbMATH, CORE, OpenAIRE, Google Scholar and others merge on shared DOIs, arXiv ids and titles, so each paper appears once with complementary metadata. A ranking model, in the cloud or on your machine, orders the pool, the strongest papers bring in their citations and references, and everything is ranked together. The architecture page shows what each stage does for you and the backend film follows one question through it in two and a half minutes.

Runtime architecture

Agents call typed MCP tools over stdio or Streamable HTTP. Full text comes from open copies first and, for licensed papers, through your library's proxy. A persistent SQLite library with FTS5 search holds papers, notes and PDF attachments under ~/.scholar-mcp/, with Markdown projections for Obsidian and connectors for Zotero and Notion.

📊 Measured retrieval quality

On the same 50 LitSearch queries, Scholar placed a correct paper in its top five for 62% of queries, against 52% for Exa research-paper search and 46% for BM25.

Results and protocol

Hit@k is the share of queries with at least one ground-truth paper in the top k; MRR averages the reciprocal rank of the first match. Scholar recovered nine top-five hits that Exa missed, and Exa four that Scholar missed. The comparison uses the first 50 LitSearch inline-ACL queries, the same title matcher and a top-20 cutoff; Scholar ran its standard pipeline with Qwen reranking, Exa used category research paper, and BM25 follows the official LitSearch title+abstract implementation over the 64K-paper corpus. The frozen summary is in docs/benchmarks/litsearch-inline-acl-50.json, with raw BM25 results and their hash manifest.

📚 Your paper library

Saved papers, PDFs and notes live in one local SQLite library and stay searchable across sessions. Obsidian receives a live Markdown projection, Zotero a synchronized collection and Notion a reading list. Notes you edit in Obsidian or Notion flow back, so your agent builds on your changes.

scholar-mcp library export obsidian --collection memory --path ~/Vault

scholar-mcp library sync zotero --collection memory --apply

💻 Command line and Python

The tools work without an agent too. Output is YAML for reading or JSON with --json.

scholar-mcp search "long-term memory benchmarks for LLM agents" --year 2024-2026

scholar-mcp read arXiv:2504.19413 --pages 1-4 scholar-mcp read arXiv:2504.19413 --visual "Figure 1" --image figure1.png scholar-mcp doctor --network

from scholar_mcp import api

hits = api.search("long-term memory benchmarks for LLM agents", year="2024-2026") paper = api.info(hits["results"]