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Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management.
Scholar RAG MCP 是 ChatHome Skills 收录的 MCP 条目，来源为 Official MCP Registry，质量分 73/100。 作为 Model Context Protocol 服务器，它向 Claude、Cursor、Copilot 等兼容 MCP 的 LLM 客户端暴露可直接调用的工具，让 agent 的能力超出模型内置范围。 该条目由 ChatHome Skills 的每日抓取从官方 registry 和 GitHub 发现查询中收录，每次运行都会重新核验仓库事实、新鲜度信号和安装元数据。 质量分综合了官方 registry 收录情况、可用安装命令、许可证完整性、仓库活跃度和 GitHub star 趋势，完整算法说明见来源页。安装命令已列于上方，可直接复制使用。 在将 Scholar RAG MCP 加入生产 agent 工作流前，请核对上方的最近更新日期和仓库状态——下方事实快照是最近一次抓取的时点值。
uvx scholar-rag-mcp==0.4.0运行此命令前，请先核对上游源码。
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-09-04T01:53:07.362043Z",
"publishedAt": "2026-09-04T01:53:07.362043Z",
"statusChangedAt": "2026-09-04T01:53:07.362043Z"
}
},
"server": {
"name": "io.github.notwhiteblank/scholar-rag-mcp",
"title": "Scholar RAG MCP",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"version": "0.4.0",
"packages": [
{
"version": "0.4.0",
"transport": {
"type": "stdio"
},
"identifier": "scholar-rag-mcp",
"registryType": "pypi",
"environmentVariables": [
{
"name": "SCHOLAR_RAG_DATA_DIR",
"description": "Data root directory (knowledge bases, caches, jobs)"
},
{
"name": "SCHOLAR_RAG_CHAT_BASE_URL",
"isRequired": true,
"description": "OpenAI-compatible chat endpoint for annotation/keywords (e.g. local vLLM)"
},
{
"name": "SCHOLAR_RAG_EMBED_BASE_URL",
"isRequired": true,
"description": "OpenAI-compatible embeddings endpoint"
},
{
"name": "SCHOLAR_RAG_RERANK_BASE_URL",
"isRequired": true,
"description": "Rerank endpoint (/v1/rerank)"
}
]
}
],
"repository": {
"url": "https://github.com/notwhiteblank/scholar-rag-mcp",
"source": "github"
},
"description": "Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management."
}
}