by kalicyh
RAG offers cloud-based vector database, semantic search, and retrieval augmented generation with fast OpenAI-powered doc
A low-latency RAG (Retrieval-Augmented Generation) service that lets you upload documents and perform semantic search using OpenAI embeddings with local vector storage. Includes both direct retrieval and LLM-powered summary modes.
RAG is a community-built MCP server published by kalicyh that provides AI assistants with tools and capabilities via the Model Context Protocol. RAG offers cloud-based vector database, semantic search, and retrieval augmented generation with fast OpenAI-powered doc It is categorized under ai ml, analytics data.
You can install RAG in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
RAG is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated RAG against two servers with overlapping tools; this profile had the clearer scope statement.
We wired RAG into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: RAG is the kind of server we cite when onboarding engineers to host + tool permissions.
RAG is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
RAG reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We evaluated RAG against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend RAG for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
RAG has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
RAG has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: RAG surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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基于 MCP (Model Context Protocol) 协议的低延迟 RAG (Retrieval-Augmented Generation) 服务架构。
# 基础安装 (仅云端API)
uv sync
# 如果需要使用本地embedding模型 (m3e-small, e5-small)
uv sync --extra local-embeddings
uv run mcp-rag serve
首次启动会报错（懒得改）
该命令同时启动 Streamable HTTP MCP 端点和管理界面，后续可以直接访问 HTTP 页面完成配置、上传与查询。
http://localhost:8060/config-pagehttp://localhost:8060/documents-pagehttp://localhost:8060/docsMCP-RAG 现在使用 JSON 文件进行持久化配置管理
data\config.json 文件存储配置信息，支持通过 Web 界面进行修改和保存。
默认配置示例：
{
"host": "0.0.0.0",
"port": 8060,
"http_port": 8060,
"debug": false,
"vector_db_type": "chroma",
"chroma_persist_directory": "./data/chroma",
"qdrant_url": "http://localhost:6333",
"embedding_provider": "zhipu",
"embedding_device": "cpu",
"embedding_cache_dir": null,
"provider_configs": {
"doubao": {
"base_url": "https://ark.cn-beijing.volces.com/api/v3",
"model": "doubao-embedding-text-240715",
"api_key": null
},
"zhipu": {
"base_url": "https://open.bigmodel.cn/api/paas/v4",
"model": "embedding-3",
"api_key": null
}
},
"llm_provider": "doubao",
"llm_model": "doubao-seed-1.6-250615",
"llm_base_url": "https://ark.cn-beijing.volces.com/api/v3",
"llm_api_key": null,
"enable_llm_summary": false,
"enable_thinking": true,
"max_retrieval_results": 5,
"similarity_threshold": 0.7,
"enable_reranker": false,
"enable_cache": false
}
注意：
- 仅测试豆包与智谱的向量模型，其他模型未测试
- 豆包的向量模型好像要下线了，不推荐使用豆包的向量模型
小智go服务端能通过 MCP 协议与 MCP-RAG 进行交互。以下是一个示例配置：
{
"mcpServers": {
"RAG": {
"url": "http://127.0.0.1:8060/mcp"
}
}
}
{
"name": "rag_ask",
"arguments": {
"query": "查询内容",
"mode": "raw",
"limit": 5
}
}
MIT License
欢迎提交 Issue 和 Pull Request！
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.