RAG offers cloud-based vector database, semantic search, and retrieval augmented generation with fast OpenAI-powered doc
by 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
Time estimate: 15-60 minutes depending on server complexity
Prerequisites
Amicus MCP Server
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Clerk
Securely manage Clerk authentication, users, sessions, orgs, and authorization for seamless identity and access control.
Context Portal
Context Portal: Manage project memory with a database-backed system for decisions, tracking, and semantic search via a k
MCP Server Chart
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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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Claude Desktop configuration
{
"mcpServers": {
"rag": {
"command": "uv",
"args": [
"run",
"mcp-rag",
"serve"
],
"env": {
"PYTHONUNBUFFERED": "1",
"OPENAI_API_KEY": "key",
"OPENAI_API_BASE": "https://ark.cn-beijing.volces.com/api/v3",
"OPENAI_MODEL": "doubao-1-5-pro-32k-250115",
"OPENAI_TEMPERATURE": "0",
"OPENAI_EMBEDDING_MODEL": "doubao-embedding-text-240715"
}
}
}
}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.
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！
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.