Agent Knowledge MCP Server

@Ddhjx-code

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

Ddhjx-code

提交者

Zijing Wang

关于 Agent Knowledge MCP Server

AI agent面试skill，可以模拟面试过程

#aiagent-agent-knowledeg

Agent Knowledge MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Agent Knowledge MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

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

常见问题

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.

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