Scholar MCP

Name: Scholar MCP
Author: Liyux3

by Liyux3version 0.10.0i★ –iRuns on your computer

Registry nameio.github.Liyux3/scholar-mcp

Install

Overview

Description from the maker (original English)

Federated academic search, citation traversal, persistent PDF download, and temporary reading.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiEarly↓ 579/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: S2_API_KEY, OPENALEX_API_KEY, CORE_API_KEY, SCOPUS_API_KEY, DASHSCOPE_API_KEY, SCHOLAR_RERANK_API_KEY, EXA_API_KEY, ZOTERO_API_KEY, NOTION_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: SCHOLAR_DATA_DIR, SCHOLAR_MCP_EXTENSIONS, S2_API_KEY, OPENALEX_API_KEY, CORE_API_KEY, SCOPUS_API_KEY, DASHSCOPE_API_KEY, DASHSCOPE_HTTP_BASE_URL, SCHOLAR_RERANK_MODEL, SCHOLAR_RERANK_URL, SCHOLAR_RERANK_API_KEY, EXA_API_KEY, ZOTERO_API_KEY, ZOTERO_LIBRARY_ID, NOTION_API_KEY, NOTION_DATA_SOURCE_ID

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "scholar": {
      "command": "uvx",
      "args": [
        "scholar-mcp"
      ],
      "env": {
        "SCHOLAR_DATA_DIR": "your-value",
        "SCHOLAR_MCP_EXTENSIONS": "your-value",
        "S2_API_KEY": "your-value",
        "OPENALEX_API_KEY": "your-value",
        "CORE_API_KEY": "your-value",
        "SCOPUS_API_KEY": "your-value",
        "DASHSCOPE_API_KEY": "your-value",
        "DASHSCOPE_HTTP_BASE_URL": "your-value",
        "SCHOLAR_RERANK_MODEL": "your-value",
        "SCHOLAR_RERANK_URL": "your-value",
        "SCHOLAR_RERANK_API_KEY": "your-value",
        "EXA_API_KEY": "your-value",
        "ZOTERO_API_KEY": "your-value",
        "ZOTERO_LIBRARY_ID": "your-value",
        "NOTION_API_KEY": "your-value",
        "NOTION_DATA_SOURCE_ID": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add scholar --env SCHOLAR_DATA_DIR=your-value --env SCHOLAR_MCP_EXTENSIONS=your-value --env S2_API_KEY=your-value --env OPENALEX_API_KEY=your-value --env CORE_API_KEY=your-value --env SCOPUS_API_KEY=your-value --env DASHSCOPE_API_KEY=your-value --env DASHSCOPE_HTTP_BASE_URL=your-value --env SCHOLAR_RERANK_MODEL=your-value --env SCHOLAR_RERANK_URL=your-value --env SCHOLAR_RERANK_API_KEY=your-value --env EXA_API_KEY=your-value --env ZOTERO_API_KEY=your-value --env ZOTERO_LIBRARY_ID=your-value --env NOTION_API_KEY=your-value --env NOTION_DATA_SOURCE_ID=your-value -- uvx scholar-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "scholar": {
      "command": "uvx",
      "args": [
        "scholar-mcp"
      ],
      "env": {
        "SCHOLAR_DATA_DIR": "your-value",
        "SCHOLAR_MCP_EXTENSIONS": "your-value",
        "S2_API_KEY": "your-value",
        "OPENALEX_API_KEY": "your-value",
        "CORE_API_KEY": "your-value",
        "SCOPUS_API_KEY": "your-value",
        "DASHSCOPE_API_KEY": "your-value",
        "DASHSCOPE_HTTP_BASE_URL": "your-value",
        "SCHOLAR_RERANK_MODEL": "your-value",
        "SCHOLAR_RERANK_URL": "your-value",
        "SCHOLAR_RERANK_API_KEY": "your-value",
        "EXA_API_KEY": "your-value",
        "ZOTERO_API_KEY": "your-value",
        "ZOTERO_LIBRARY_ID": "your-value",
        "NOTION_API_KEY": "your-value",
        "NOTION_DATA_SOURCE_ID": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "scholar": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "scholar-mcp"
      ],
      "env": {
        "SCHOLAR_DATA_DIR": "your-value",
        "SCHOLAR_MCP_EXTENSIONS": "your-value",
        "S2_API_KEY": "your-value",
        "OPENALEX_API_KEY": "your-value",
        "CORE_API_KEY": "your-value",
        "SCOPUS_API_KEY": "your-value",
        "DASHSCOPE_API_KEY": "your-value",
        "DASHSCOPE_HTTP_BASE_URL": "your-value",
        "SCHOLAR_RERANK_MODEL": "your-value",
        "SCHOLAR_RERANK_URL": "your-value",
        "SCHOLAR_RERANK_API_KEY": "your-value",
        "EXA_API_KEY": "your-value",
        "ZOTERO_API_KEY": "your-value",
        "ZOTERO_LIBRARY_ID": "your-value",
        "NOTION_API_KEY": "your-value",
        "NOTION_DATA_SOURCE_ID": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

SCHOLAR_DATA_DIR env
SCHOLAR_MCP_EXTENSIONS env
S2_API_KEY secret env
OPENALEX_API_KEY secret env
CORE_API_KEY secret env
SCOPUS_API_KEY secret env
DASHSCOPE_API_KEY secret env
DASHSCOPE_HTTP_BASE_URL env
Alibaba workspace API host or /api/v1 base URL.
SCHOLAR_RERANK_MODEL env
Alibaba or custom reranker model name.
SCHOLAR_RERANK_URL env
Full custom rerank endpoint; overrides Alibaba routing.
SCHOLAR_RERANK_API_KEY secret env
EXA_API_KEY secret env
ZOTERO_API_KEY secret env
ZOTERO_LIBRARY_ID env
NOTION_API_KEY secret env
NOTION_DATA_SOURCE_ID env

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 10, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Liyux3, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5845 servers in this category
Published to the registry more recently than 90% of the other 5845 servers in this category
Basic checks equal to or better than 100% of the other 5845 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Scholar MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: S2_API_KEY, OPENALEX_API_KEY, CORE_API_KEY, SCOPUS_API_KEY, DASHSCOPE_API_KEY, SCHOLAR_RERANK_API_KEY, EXA_API_KEY, ZOTERO_API_KEY, NOTION_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.10.0) reached the registry 4 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

by upstashWidely used↓ 972.7K/wk★ 64.2KUpdated yesterday

Computer or cloud

Install

Codebase Memory

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

by DeusDataWidely used↓ 18K/wk★ 48.1KUpdated 24 days ago

Runs on your computer

Install

Exa

Connect AI agents to Exa for web search, content fetching, and multi-step research.

by exa-labsWidely used★ 5.2KUpdated 2 months ago

Runs in the cloud✓ exa.ai

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

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]"

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.

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

	Tool
Search	`search_papers`	One question fans out to every source; duplicates merge into one record, the pool is reranked, and citations of the best matches are followed before the final ranking
Inspect	`paper_info`	Full metadata, citations and references of a paper in one call
Explore	`recommend_papers`	Semantic neighbours, co-cited peers and papers sharing references
People	`search_authors`	Author profiles, affiliations and metrics
Read	`read_paper`	Page-aware Markdown, structured tables and a focused image of any figure, read temporarily
Keep	`download_paper`	The PDF from open archives, repositories or your institution's library, filed in your library
Map	`build_paper_graph`	A citation graph with PageRank and bridge papers (research profile)
Remember	`paper_library`	Collections, notes, tags and full-text search, mirrored to Obsidian, Zotero or Notion (research profile)

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

System	Hit@5	Hit@10	Hit@20	MRR
Scholar	0.62	0.68	0.70	0.442
Exa `research paper`	0.52	0.58	0.64	0.435
BM25 `title + abstract`	0.46	0.46	0.56	0.335

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"]

Only the beginning is shown; read the rest in the repo

Scholar MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

📰 News

🚀 Get started

🧰 What your agent can do

🧭 How it works

📊 Measured retrieval quality

📚 Your paper library

💻 Command line and Python