MCP.so
ログイン

Agent Knowledge

@Ddhjx-code

リポジトリ

Agent Knowledge について

AI agent面试skill，可以模拟面试过程

#aiagent-agent-knowledeg

基本情報

カテゴリ

AI とエージェント

ランタイム

python

トランスポート

stdio

公開者

Ddhjx-code

投稿者

Zijing Wang

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "interview-rag": {
      "command": "uvx",
      "args": [
        "mcp-server-interview-rag"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Agent Knowledge?

Agent Knowledge is an MCP (Model Context Protocol) server that powers AI Agent job interview simulations with a RAG knowledge base. It integrates FAISS vector search and a Chinese embedding model (BAAI/bge-base-zh-v1.5) to provide semantic retrieval from the hello-agents tutorial and Agent-Learning-Hub external resources. It is designed for developers preparing for AI Agent technical interviews.

How to use Agent Knowledge?

Install dependencies (pip install -r interview_rag_server/requirements.txt), build the FAISS index by running the provided scripts, then configure the MCP server in .claude/settings.json. Use it through Claude Code’s /interview skill; no direct invocation is needed.

Key features of Agent Knowledge

  • Semantic knowledge retrieval via FAISS vector index (1086 vectors, 768-dim)
  • Three MCP tools: search_knowledge, get_interview_questions, get_learning_path
  • Uses BAAI/bge-base-zh-v1.5 for Chinese embeddings
  • Knowledge sources include 16-chapter tutorial and 90+ external resources
  • Zero-infrastructure, single-file persistence for vector store

Use cases of Agent Knowledge

  • Real-time knowledge lookup during AI Agent mock interviews
  • Dynamically retrieving interview questions based on candidate’s weak topics
  • Generating personalized learning paths for interview preparation

FAQ from Agent Knowledge

What dependencies are required?

Python packages: fastmcp>=2.0.0, faiss-cpu>=1.7.4, sentence-transformers>=2.2.0.

How is the knowledge base built?

Run python -m interview_rag_server.knowledge_base.build_index after cloning two source repos and fetching web sources. The index is created under interview_rag_server/data/.

Where is the data stored?

The FAISS index (faiss_index.bin) and metadata (metadata.json) reside in the interview_rag_server/data/ directory.

What parameters does search_knowledge accept?

It accepts query (required), topic (optional), and top_k (optional).

Can Agent Knowledge run offline?

Yes, set the environment variable HF_HUB_OFFLINE=1 and ensure the embedding model is cached locally.

コメント

「AI とエージェント」の他のコンテンツ

1Panel

1Panel-dev

🔥 1Panel is a modern, open-source VPS control panel — and the only one with native AI agent support. Run Ollama models, deploy OpenClaw agents, and manage your entire server stack from one clean web interface.

36.1K

Web Agent Protocol

OTA-Tech-AI

🌐Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support

500

Shell and Coding agent for Claude and other mcp clients

rusiaaman

Shell and coding agent on mcp clients

666

21st.dev Magic AI Agent

21st-dev

It's like v0 but in your Cursor/WindSurf/Cline. 21st dev Magic MCP server for working with your frontend like Magic

5.3K

Solon Ai

opensolon

Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java25. It can also be embedded in SpringBoot, jFinal, Vert.x, Quarkus, and other frameworks.

416