An MCP server for searching and retrieving articles from Google Scholar
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"google-scholar-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# Google Scholar MCP Server & Skill 🔍 让 AI 助手通过 MCP 协议或 OpenClaw Skill 搜索和访问 Google Scholar 学术论文。 本项目提供两种使用方式： | 方式 | 适用场景 | 协议 | |------|----------|------| | **MCP Server** | Claude Desktop / Cursor / VS Code / Cline 等支持 MCP 的客户端 | Model Context Protocol (stdio) | | **OpenClaw Skill** | OpenClaw / QClaw 平台的 Agent 技能包 | OpenClaw Skill 协议 | --- ## ✨ 核心功能 - 🔎 **论文搜索**：支持关键词搜索、作者筛选、年份范围筛选 - 📄 **完整摘要**：提供精确标题和作者时可获取完整摘要内容 - 🛡️ **验证码处理**：优化了 scholarly 库的 CAPTCHA 验证码处理机制，遇到验证码时自动弹出浏览器窗口供手动验证 - 📚 **BibTeX 支持**：可获取论文的 BibTeX 引用格式 - 🚀 **高效检索**：快速获取论文元数据 --- ## 📦 方式一：MCP Server ### MCP 工具 #### search_google_scholar 搜索 Google Scholar 上的学术文章。 **参数：** | 参数 | 类型 | 必填 | 说明 | |------|------|------|------| | `query` | string | ✅ | 搜索关键词（论文标题、主题或关键词） | | `author` | string | ❌ | 作者名称筛选 | | `year_low` | int | ❌ | 起始年份 | | `year_high` | int | ❌ | 结束年份 | | `num_results` | int | ❌ | 返回结果数量（默认: 5） | **返回结果：** ```json { "bib": { "title": "论文标题", "author": "作者", "pub_year": "发表年份", "venue": "发表期刊/会议", "abstract": "摘要" }, "pub_url": "论文链接", "num_citations": "被引用次数", "citedby_url": "引用链接", "eprint_url": "PDF 链接" } ``` ### 使用示例 **关键词搜索：** ```python result = await mcp.use_tool("search_google_scholar", { "query": "deep learning", "num_results": 5 }) ``` **带作者筛选：** ```python result = await mcp.use_tool("search_google_scholar", { "query": "convolutional neural networks", "author": "Yann LeCun", "num_results": 3 }) ``` **带年份范围：** ```python result = await mcp.use_tool("search_google_scholar", { "query": "transformer", "year_low": 2020, "year_high": 2024, "num_results": 5 }) ``` **组合搜索：** ```python result = await mcp.use_tool("search_google_scholar", { "query": "neural networks", "author": "Geoffrey Hinton", "year_low": 2015, "year_high": 2023, "num_results": 10 }) ``` ### 🐍 作为 Py…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.