Claude 智能体深度评测：从 MCP 到 Computer Use

MCP（工具协议）→ Computer Use（桌面代理）→ Artifacts（内容生成）
    标准化工具        操控电脑              交互式输出

AI 模型 (Client)
    ↕ MCP 协议
MCP Server
    ↕
工具/数据源

from mcp import Server, Tool

server = Server("my-tools")

@server.tool()
async def search_db(query: str, limit: int = 10):
    """搜索数据库"""
    results = await db.search(query, limit)
    return {"results": results}

@server.tool()
async def create_chart(data: list[dict], chart_type: str = "bar"):
    """创建图表"""
    chart = ChartBuilder(data, chart_type).build()
    return {"chart_url": chart.save()}

@server.resource()
async def get_schema():
    """暴露数据库 schema"""
    return await db.get_schema()

# 启动 MCP Server
server.run(transport="stdio")  # 或 "sse"

# 2026 年 MCP 生态
mcp_servers = {
    # 官方
    "filesystem": "文件系统操作",
    "github": "GitHub 仓库管理",
    "postgres": "数据库查询",
    "slack": "消息发送",
    "google-drive": "文档访问",
    
    # 社区
    "notion": "Notion 页面操作",
    "linear": "项目管理",
    "figma": "设计稿读取",
    "jira": "工单管理",
    "shell": "命令行执行",
}

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-4-sonnet",
    max_tokens=4096,
    tools=[{"type": "computer_20250124", "display_width_px": 1920, "display_height_px": 1080}],
    messages=[{
        "role": "user",
        "content": "打开 Excel，创建一个包含销售数据的表格，保存到桌面"
    }]
)

# Claude 的操作序列：
# 1. screenshot() → 看到桌面
# 2. click(Excel图标) → 打开 Excel
# 3. type("产品名\t销量\t收入\n") → 输入表头
# 4. type("产品A\t100\t5000\n") → 输入数据
# 5. key("ctrl+s") → 保存
# 6. type("销售数据.xlsx") → 文件名
# 7. click(保存按钮) → 完成

artifact_types = {
    "code": "代码片段（高亮+复制）",
    "documents": "Markdown 文档（渲染）",
    "html": "交互式网页（实时预览）",
    "svg": "矢量图形（渲染）",
    "mermaid": "流程图（渲染）",
    "react": "React 组件（实时渲染）",
}

# 用户说"做一个贪吃蛇游戏"
# Claude 直接输出可玩的 HTML5 游戏
# 无需切换到外部编辑器

# Claude 的 Agent 优势
claude_strengths = {
    "长上下文": "200K tokens，适合长文档处理",
    "MCP生态": "标准化工具协议，生态丰富",
    "Artifacts": "交互式输出，用户体验好",
    "Computer Use": "桌面级自动化",
    "代码能力": "HumanEval 85.1%，代码生成领先",
    "推理能力": "思维链推理能力强，适合复杂任务",
}

# Claude 的劣势
claude_weaknesses = {
    "多模态": "图像能力弱于 GPT-4o",
    "生态": "用户量少于 OpenAI",
    "价格": "比 DeepSeek 贵 20x",
    "中文": "中文能力略弱于国产模型",
}

from anthropic import Anthropic
from mcp import MCPClient

# 1. 连接 MCP Servers
client = MCPClient()
client.connect("postgres-server")    # 数据库
client.connect("slack-server")       # 消息通知
client.connect("zendesk-server")     # 工单系统

# 2. 获取工具列表
tools = await client.list_tools()
# 自动发现：query_orders, send_slack, create_ticket...

# 3. 构建 Agent
claude = Anthropic()

async def customer_service(user_message):
    response = claude.messages.create(
        model="claude-4-sonnet",
        system="你是客服 Agent，可以查询订单、创建工单、通知团队。",
        tools=tools,
        messages=[{"role": "user", "content": user_message}]
    )
    
    # 处理工具调用
    while response.stop_reason == "tool_use":
        tool_results = await client.execute_tools(response.tool_calls)
        
        response = claude.messages.create(
            model="claude-4-sonnet",
            tools=tools,
            messages=[
                {"role": "user", "content": user_message},
                response,
                {"role": "tool", "content": tool_results}
            ]
        )
    
    return response.content