在构建多Agent系统时，工具隔离是一个关键需求。通过Starlette和FastMCP，我们可以创建一个支持多路由隔离的工具服务器，让不同Agent调用各自专属的工具集。

核心思路：路由隔离工具集

from starlette.applications import Starlette
from starlette.routing import Mount
from mcp.server.fastmcp import FastMCP

# 创建多个独立的MCP实例
github_mcp = FastMCP("GitHub API")
browser_mcp = FastMCP("Browser")
curl_mcp = FastMCP("Curl")
search_mcp = FastMCP("Search")

# 应用配置
app = Starlette(
    routes=[
        Mount("/github", app=github_mcp.sse_app()),
        Mount("/browser", app=browser_mcp.sse_app()),
        Mount("/curl", app=curl_mcp.sse_app()),
        Mount("/search", app=search_mcp.sse_app()),
    ]
)

这种架构的关键优势在于：

1. 工具隔离：每个路由下的工具相互独立
2. 模块化设计：按功能划分工具组
3. 独立扩展：不同工具集可以单独扩展和部署
4. 权限控制：为不同Agent分配不同路由权限

工具定义示例

GitHub工具集（/github路由）

@github_mcp.tool()
async def search_github_repos(query: str, limit: int = 10) -> Dict[str, Any]:
    """搜索GitHub仓库"""
    url = f"https://api.github.com/search/repositories?q={query}&per_page={limit}"
    response = requests.get(url)
    return response.json()

浏览器工具集（/browser路由）

@browser_mcp.tool()
async def get_webpage_title(url: str) -> Dict[str, Any]:
    """获取网页标题"""
    try:
        response = requests.get(url, timeout=5)
        # 简单提取标题
        import re
        title_match = re.search(r'<title>(.*?)</title>', response.text, re.IGNORECASE | re.DOTALL)
        title = title_match.group(1) if title_match else "未找到标题"
        return {"url": url, "title": title}
    except Exception as e:
        return {"url": url, "error": str(e)}

客户端调用示例

不同Agent可以访问各自的路由：

# GitHub Agent
github_client = Client("http://localhost:8000/github/sse")
await github_client.call_tool("search_github_repos", {"query": "AI"})

# 浏览器Agent
browser_client = Client("http://localhost:8000/browser/sse")
await browser_client.call_tool("get_webpage_title", {"url": "https://example.com"})

架构优势分析

1. 职责分离：每个工具组专注于特定领域
2. 安全隔离：敏感工具（如curl）可单独保护
3. 性能优化：高负载工具可独立扩展
4. 版本管理：不同工具组可独立升级

实际应用场景

1. 多Agent协作系统：不同Agent使用不同工具集
2. 微服务架构：每个路由作为独立微服务
3. 权限分级系统：根据用户角色分配路由访问权限
4. A/B测试环境：不同路由部署不同版本工具

总结

通过Starlette的路由挂载功能和FastMCP的工具管理能力，我们可以构建高度模块化的Agent工具服务器。这种架构不仅实现了工具隔离，还为系统的可扩展性和安全性提供了坚实基础。

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

启动服务器后，不同Agent就可以通过各自的路由访问专属工具集，实现安全、高效的协作。

mcp 服务

from starlette.applications import Starlette
from starlette.routing import Mount
from mcp.server.fastmcp import FastMCP

import requests

from typing import Dict, Any


# Create multiple MCP servers
github_mcp = FastMCP("GitHub API")
browser_mcp = FastMCP("Browser")
curl_mcp = FastMCP("Curl")
search_mcp = FastMCP("Search")


@github_mcp.tool()
async def search_github_repos(query: str, limit: int = 10) -> Dict[str, Any]:
    """
    搜索GitHub仓库
    
    Args:
        query: 搜索关键词
        limit: 返回结果数量限制，默认10个
        
    Returns:
        包含仓库信息的字典
    """
    url = f"https://api.github.com/search/repositories?q={query}&per_page={limit}"
    response = requests.get(url)
    return response.json()


@browser_mcp.tool()
async def get_webpage_title(url: str) -> Dict[str, Any]:
    """
    获取网页标题
    
    Args:
        url: 网页地址
        
    Returns:
        包含网页标题的字典
    """
    try:
        response = requests.get(url, timeout=5)
        # 简单提取标题
        import re
        title_match = re.search(r'<title>(.*?)</title>', response.text, re.IGNORECASE | re.DOTALL)
        title = title_match.group(1) if title_match else "未找到标题"
        return {"url": url, "title": title}
    except Exception as e:
        return {"url": url, "error": str(e)}


@curl_mcp.tool()
async def make_http_request(url: str, method: str = "GET", headers: Dict[str, str] = None) -> Dict[str, Any]:
    """
    发起HTTP请求
    
    Args:
        url: 请求地址
        method: HTTP方法，默认GET
        headers: 请求头
        
    Returns:
        HTTP响应结果
    """
    try:
        if headers is None:
            headers = {}
            
        response = requests.request(method, url, headers=headers)
        return {
            "status_code": response.status_code,
            "headers": dict(response.headers),
            "content": response.text[:1000]  # 限制返回内容长度
        }
    except Exception as e:
        return {"error": str(e)}


@search_mcp.tool()
async def search_text(query: str, texts: list) -> Dict[str, Any]:
    """
    在文本列表中搜索关键词
    
    Args:
        query: 搜索关键词
        texts: 要搜索的文本列表
        
    Returns:
        包含匹配文本的列表
    """
    results = []
    for i, text in enumerate(texts):
        if query.lower() in text.lower():
            results.append({"index": i, "text": text})
    return {"query": query, "results": results}


app = Starlette(
    routes=[
        # Using settings-based configuration
        Mount("/github", app=github_mcp.sse_app()),
        Mount("/browser", app=browser_mcp.sse_app()),
        Mount("/curl", app=curl_mcp.sse_app()),
        Mount("/search", app=search_mcp.sse_app()),
    ]
)


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

客户请求

import asyncio
from fastmcp import Client

client = Client("http://localhost:8000/search/sse")

async def call_tool(name: str):
    async with client:
        result = await client.list_tools()
        print(result)
        result = await client.call_tool("search_text", {"query": name,"texts":["dfdsf","sdfsdfsf"]})
        print(result)

asyncio.run(call_tool("Ford"))




client = Client("http://localhost:8000/curl/sse")

async def call_tool(name: str):
    async with client:
        result = await client.list_tools()
        print(result)


asyncio.run(call_tool("Ford"))





client = Client("http://localhost:8000/browser/sse")

async def call_tool(name: str):
    async with client:
        result = await client.list_tools()
        print(result)


asyncio.run(call_tool("Ford"))


client = Client("http://localhost:8000/github/sse")

async def call_tool(name: str):
    async with client:
        result = await client.list_tools()
        print(result)


asyncio.run(call_tool("Ford"))