双服务器架构革命：基于MCP+LangGraph的智能研究助手全实现

npm install -g @mcp-servers/firecrawl  # 安装命令

from langgraph.graph import StateGraph

workflow = StateGraph(AgentState)
workflow.add_node("research", research_tool_node)
workflow.add_node("web_crawl", firecrawl_node)
workflow.add_conditional_edges(
    "agent",
    decide_next_action,
    branches={"research": "research", "web": "web_crawl"}
)  # 动态路由逻辑

@mcp_tool
def pdf_extractor(url: str) -> str:
    """Firecrawl网页PDF提取工具"""
    return firecrawl_api.scrape(url, params={"extract_pdf": True})

if user_input.startswith("@prompt:"):
    load_prompt(user_input.split(":")[1])  # 动态提示加载
elif user_input.startswith("@use_resource:"):
    uri, query = parse_resource_cmd(user_input)
    execute_resource_tool(uri, query)       # 按需资源调用

git clone https://github.com/example/mcp-research-assistant
cd mcp-research-assistant && uv sync  # 依赖安装

# config.yaml
firecrawl:
  api_key: YOUR_API_KEY
  mcp_endpoint: "stdio"  # 或 https://api.firecrawl.io

# 终端1：启动自定义MCP服务器
python research_server.py --port 8033

# 终端2：启动Firecrawl服务
firecrawl-mcp --transport stdio

{
  "tool_output": "10篇相关论文摘要...",
  "next_step": "是否需要深度网络搜索？"
}