内容
# 🧠 arxiv-daily-mcp
> **A Model Context Protocol (MCP) server that wraps an arXiv CLI workflow.**
> Search arXiv in , , , or — and save findings to a local Markdown vault.
[](LICENSE)
[](https://www.python.org)
[](https://modelcontextprotocol.io)
[](https://github.com/nerin81-netizen/arxiv-daily-mcp/stargazers)
**🌍 Read in your language:** [English](README.md) · [한국어](README.ko.md) · [中文](README.zh.md) · [日本語](README.ja.md) · [Español](README.es.md) · [Français](README.fr.md) · [हिन्दी](README.hi.md) · [العربية](README.ar.md)
[🚀 Quickstart](#-quickstart) · [🛠 Tools](#-tools-4) · [🎬 Demo](#-demo) · [🆚 vs other arXiv MCPs](#-how-it-differs-from-existing-arxiv-mcps) · [🏗 Architecture](#-architecture) · [🧪 Development](#-development) · [🌐 Multilingual](#-multilingual-keywords)
---
## ✨ What this is
A simple MCP server that exposes a useful arXiv workflow as LLM-callable tools. The core idea:
> **Take a CLI script that you already run every day, and wrap it in MCP so you can invoke it with natural language from any MCP client.**
If you've ever found yourself typing the same `curl` / `python` / `arxiv` query into a terminal every morning — this is what that looks like as an MCP tool.
## 🛠 Tools (4)
| Tool | Args | Description | Example prompt |
|---|---|---|---|
| `get_today_papers` | `category`, `max_results` | Today's AI / business papers | "오늘 AI 논문 알려줘" |
| `search_papers` | `keyword`, `max_results` | **Multilingual** keyword search | "에이전트 관련 논문" / "智能体 论文" / "エージェント 論文" |
| `save_paper_note` | `arxiv_id`, `note` | Save a paper to local `.md` vault | "이거 메모해줘" |
| `list_my_notes` | `keyword`, `limit` | Search / list saved notes | "내 메모 보여줘" |
### `get_today_papers(category, max_results=5)`
- `category`: `"ai"` (cs.AI+cs.CL+cs.LG) · `"business"` (econ.GN+q-fin) · `"all"` (전체)
- Returns: Markdown card-news list of today's submissions
### `search_papers(keyword, max_results=5)`
- `keyword`: any text in , , , or
- Auto-translates non-English keywords to English for arXiv search
- Returns: Markdown card-news of results, with translation note
### `save_paper_note(arxiv_id, note="")`
- `arxiv_id`: `"2507.01234"` or full URL `https://arxiv.org/abs/2507.01234`
- `note`: your commentary (optional)
- Returns: path to saved file (default: `~/memos/YYYY-MM-DD_arxiv_<id>.md`)
### `list_my_notes(keyword="", limit=10)`
- `keyword`: filter by title / content (optional)
- Returns: list of recently saved note files
## 🚀 Quickstart
### Install
```bash
pip install arxiv-daily-mcp
```
Or from source:
```bash
git clone https://github.com/nerin81-netizen/arxiv-daily-mcp
cd arxiv-daily-mcp
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
```
### Register with your MCP client
#### Claude Desktop
`~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"arxiv-daily": {
"command": "arxiv-daily-mcp"
}
}
}
```
#### Cursor / Cline
`.cursor/mcp.json` or workspace settings:
```json
{
"mcpServers": {
"arxiv-daily": {
"command": "arxiv-daily-mcp"
}
}
}
```
#### Any MCP-compatible client
This server uses the standard `stdio` transport and exposes 4 tools via JSON-RPC — it works with any client that follows the [MCP spec](https://modelcontextprotocol.io).
## 🎬 Demo
### Korean keyword (auto-translated to English)
```text
👤 에이전트 관련 논문 찾아줘
🤖 Claude:
🔍 '에이전트' 검색 결과 — → 영어: 'agent' (5편)
1. **Distributed Attacks in Persistent-State AI Control**
👥 Josh Hills, Ida Caspary, Asa Cooper Stickland
📅 2026-07-02 | 🏷️ cs.AI
🔗 https://arxiv.org/abs/2607.02514v1
```
### Chinese keyword
```text
👤 智能体 相关论文
🤖 Claude:
🔍 '智能体' 검색 결과 — → 영어: 'agent' (5편)
```
### Japanese keyword
```text
👤 エージェント 論文を探して
🤖 Claude:
🔍 'エージェント' 검색 결과 — → 영어: 'agent' (5편)
```
### Save + retrieve
```text
👤 첫 번째 논문 메모해줘 — 멀티에이전트 보안 흥미롭다
🤖 Claude:
✅ 저장 완료: `~/memos/2026-07-06_arxiv_2607.02514.md`
👤 내 메모 보여줘
🤖 Claude:
📚 내 arXiv 노트 (1개)
· `2026-07-06_arxiv_2607.02514.md` — Distributed Attacks in Persistent-State AI Control
```
## 🆚 How it differs from existing arXiv MCPs
GitHub has **466+ arXiv MCP repositories** (as of 2026-07-05). The most popular, `blazickjp/arxiv-mcp-server`, is excellent — but built for a different audience.
| | `blazickjp/arxiv-mcp-server` | `mcp-simple-arxiv` | `arxiv-daily-mcp` (this) |
|---|---|---|---|
| **Keyword languages** | English only | English only | **4 languages** |
| **Output format** | JSON / raw data | Text only | **Markdown card-news** (emoji + bullets) |
| **Note archive** | ❌ | ❌ | ✅ **Local `.md` vault** |
| **Note search** | ❌ | ❌ | ✅ `list_my_notes(keyword=…)` |
| **Status** | Maintained | Maintained | Active (v0.1.0) |
### Three reasons it can coexist
1. **Different language audience.** The 2,933-star leader is English-only. This server is designed for non-English researchers first.
2. **Different output shape.** The leader returns raw JSON / data tables. This server returns **card-news Markdown** — readable directly inside any chat UI.
3. **Different workflow.** The leader is search-and-read. This server includes **archival**: save a paper to your local Markdown vault with one sentence, then `list_my_notes` to recall it later.
## 🌐 Multilingual keywords
The translation dictionary ships with the package — **no API call, no latency, no rate limit**.
| Language | Keywords |
|---|---|
| (28) | 에이전트, 인공지능, 머신러닝, 딥러닝, 자연어처리, 컴퓨터비전, 강화학습, 생성형, 변압기, 확산모델, 검색증강, 멀티모달, 로보틱스, 추천시스템, 그래프신경망, 파운데이션모델, 거대언어모델, 프롬프트, 미세조정, 정렬, 안전성, 환각, 추론, 계획, 도구사용, 메모리, 체인, … |
| (23) | 智能体, 人工智能, 机器学习, 深度学习, 自然语言处理, 计算机视觉, 强化学习, 生成式, 变压器, 扩散模型, 多模态, 机器人, 推荐系统, 图神经网络, 基础模型, 大语言模型, 提示, 微调, 对齐, 推理, 工具使用, 链, 代理 |
| (24) | エージェント, 人工知能, 機械学習, 深層学習, 自然言語処理, コンピュータビジョン, 強化学習, 生成, 変圧器, 拡散モデル, 検索拡張, マルチモーダル, ロボット, 推薦システム, グラフニューラルネットワーク, 基盤モデル, 大規模言語モデル, プロンプト, 微調整, アライメント, 推論, 計画, ツール使用, 記憶, 幻覚 |
**Detection algorithm:**
1. If the input contains Hangul (가-힣) → Korean lookup
2. Else if it contains Kana (ぁ-ヿ) → Japanese lookup
3. Else if it contains CJK ideographs (一-鿿) → Chinese lookup
4. Else → English (passed through unchanged)
Unknown keywords are passed through as-is — so a partial match still searches arXiv directly.
## 🏗 Architecture
```
┌─────────────────┐ stdio/JSON-RPC ┌──────────────────────┐
│ LLM Client │ ◀──────────────────────▶ │ arxiv-daily-mcp │
│ (Claude/Cursor) │ │ (FastMCP server) │
└─────────────────┘ └──────────┬───────────┘
│ urllib
▼
┌──────────────┐
│ arXiv API │
│ export.arxiv│
└──────────────┘
│
▼ (save_paper_note)
┌──────────────┐
│ ./memos/ │
│ *.md vault │
└──────────────┘
```
**Key design choices:**
- **stdio transport** — MCP standard
- **FastMCP** — Anthropic's official Python SDK
- **urllib only** — zero external HTTP dependencies (works in any minimal Python environment)
- **In-process translation dictionary** — no LLM call, no rate limit, no per-keyword latency
- **Configurable MEMO_DIR** — set via env var for Docker / multi-user setups
## 🧪 Development
```bash
# Install dev dependencies
pip install -e ".[dev]"
# Run MCP Inspector (browser UI)
npx @modelcontextprotocol/inspector arxiv-daily-mcp
# Test stdio JSON-RPC directly
echo '{"jsonrpc":"2.0","method":"tools/list","id":1}' | arxiv-daily-mcp
```
### Unit test the translation layer
```python
from arxiv_daily_mcp.server import _translate_keyword
assert _translate_keyword("에이전트") == ("agent", "ko")
assert _translate_keyword("智能体") == ("agent", "zh")
assert _translate_keyword("エージェント") == ("agent", "ja")
assert _translate_keyword("agent") == ("agent", "en")
```
## 🌍 Roadmap
- [x] v0.1.0 — 4 tools, stdio, multilingual (75 keywords)
- [ ] v0.2.0 — `weekly_summary` tool (Friday digest)
- [ ] v0.3.0 — More categories (cs.RO, cs.CV, q-fin.ST)
- [ ] v0.4.0 — PyPI release
- [ ] v1.0.0 — HTTP transport (remote access)
- [ ] v1.1.0 — More languages (Spanish, French, German, Hindi)
- [ ] v2.0.0 — Note format plugins (Notion, Apple Notes, Bear)
## 🤝 Contributing
Issues and PRs welcome. Particularly useful:
- 🌏 More language keywords (especially in cs.RO, q-fin, stat.ML)
- 📂 New paper categories
- 📝 Note format options (Notion API, Apple Notes, Bear)
- 🐛 Bug reports with reproduction steps
## 📜 License
MIT
## 🙏 Credits
- Built with [Anthropic MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
- Paper data from [arXiv.org](https://arxiv.org) (open API)
- Inspired by the [modelcontextprotocol/servers](https://github.com/modelcontextprotocol/servers) reference collection
---
> 🌟 If this MCP saved you a few minutes of research workflow, consider starring the repo — it helps others find it.
