1. Conduid
  2. AI
  3. LLM MCP RAG
MCP server · AI

LLM MCP RAG

by · github.com/KelvinQiu802/llm-mcp-rag

LLM + MCP + RAG = Magic

Unclaimed last commit a year ago ai
InstallView source
59Fair

Scored 3 months ago · breakdown

About LLM MCP RAG

LLM MCP RAG is an MCP server published by KelvinQiu802 in the AI category: lLM + MCP + RAG = Magic. It has been installed 0 times through Conduid.

The repository has 492 stars and 91 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

Install
npx llm-mcp-rag

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Ask AI

✦

Ask AI about LLM MCP RAG

Powered by Claude · Grounded in docs

I know everything about LLM MCP RAG. Ask me about installation, configuration, usage, or troubleshooting.

Security checks

  • ·README presentNot checked yet.
  • ·License declaredNot checked yet.
  • ·Tests presentNot checked yet.
  • ·Dependencies pinnedNot checked yet.
  • ·No dynamic code executionNot checked yet.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

LLM + MCP + RAG

目标

  • Augmented LLM (Chat + MCP + RAG)
  • 不依赖框架
    • LangChain, LlamaIndex, CrewAI, AutoGen
  • MCP
    • 支持配置多个MCP Serves
  • RAG 极度简化板
    • 从知识中检索出有关信息，注入到上下文
  • 任务
    • 阅读网页 → 整理一份总结 → 保存到文件
    • 本地文档 → 查询相关资料 → 注入上下文

The augmented LLM

image.png

classDiagram
    class Agent {
        +init()
        +close()
        +invoke(prompt: string)
        -mcpClients: MCPClient[]
        -llm: ChatOpenAI
        -model: string
        -systemPrompt: string
        -context: string
    }
    class ChatOpenAI {
        +chat(prompt?: string)
        +appendToolResult(toolCallId: string, toolOutput: string)
        -llm: OpenAI
        -model: string
        -messages: OpenAI.Chat.ChatCompletionMessageParam[]
        -tools: Tool[]
    }
    class EmbeddingRetriever {
        +embedDocument(document: string)
        +embedQuery(query: string)
        +retrieve(query: string, topK: number)
        -embeddingModel: string
        -vectorStore: VectorStore
    }
    class MCPClient {
        +init()
        +close()
        +getTools()
        +callTool(name: string, params: Record<string, any>)
        -mcp: Client
        -command: string
        -args: string[]
        -transport: StdioClientTransport
        -tools: Tool[]
    }
    class VectorStore {
        +addEmbedding(embedding: number[], document: string)
        +search(queryEmbedding: number[], topK: number)
        -vectorStore: VectorStoreItem[]
    }
    class VectorStoreItem {
        -embedding: number[]
        -document: string
    }

    Agent --> MCPClient : uses
    Agent --> ChatOpenAI : interacts with
    ChatOpenAI --> ToolCall : manages
    EmbeddingRetriever --> VectorStore : uses
    VectorStore --> VectorStoreItem : contains

依赖

git clone git@github.com:KelvinQiu802/ts-node-esm-template.git
pnpm install
pnpm add dotenv openai @modelcontextprotocol/sdk chalk**

LLM

MCP

RAG

向量

  • 维度
  • 模长
  • 点乘 Dot Product
    • 对应位置元素的积，求和
  • 余弦相似度 cos
    • 1 → 方向完全一致
    • 0 → 垂直
    • -1 → 完全想法

image.png

image.png

image.png

image.png

README mirrored from the source repository 3 months ago. The original is authoritative.

Questions

About LLM MCP RAG

How do I install LLM MCP RAG?

Run npx llm-mcp-rag, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is LLM MCP RAG safe to use with an AI agent?

Its trust score is 59 out of 100 (fair). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is LLM MCP RAG still maintained?

The last commit was a year ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.