Loading…
感知 RAG MCP 服务 —— 不只是把文档检索结果丢给 AI，而是告诉 AI 每个结果从哪来、有多可信、用了什么策略。通过 MCP 协议暴露完整的混合检索管线（稠密向量 + BM25 关键词 + RRF 融合 + CrossEncoder 重排序），3 行 JSON 配置即可为 Claude Desktop、Cursor、Cline 等任何支持 MCP 的 AI 应用接入高质量知识检索。无需写 LangChain 胶水代码，无需管理向量库连接——入库、检索、重排序全部开箱即用。
rag_mcp_server is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 33/100. 感知 RAG MCP 服务 —— 不只是把文档检索结果丢给 AI，而是告诉 AI 每个结果从哪来、有多可信、用了什么策略。通过 MCP 协议暴露完整的混合检索管线（稠密向量 + BM25 关键词 + RRF 融合 + CrossEncoder 重排序），3 行 JSON 配置即可为 Claude Desktop、Cur. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding rag_mcp_server to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/Cheer-Beep/rag_mcp_server.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "Cheer-Beep/rag_mcp_server"
}感知 RAG MCP 服务 —— 不只是把文档检索结果丢给 AI，而是告诉 AI 每个结果从哪来、有多可信、用了什么策略。通过 MCP 协议暴露完整的混合检索管线（稠密向量 + BM25 关键词 + RRF 融合 + CrossEncoder 重排序），3 行 JSON 配置即可为 Claude Desktop、Cursor、Cline 等任何支持 MCP 的 AI 应用接入高质量知识检索。无需写 LangChain 胶水代码，无需管理向量库连接——入库、检索、重排序全部开箱即用。