{  "name": "mcp-server-browser",  "version": "0.0.1",  "type": "module",  "bin": {    "mcp-server-browser": "dist/index.cjs"  },  "main": "dist/server.cjs",  "module": "dist/server.js",  "types": "dist/server.d.ts",  "files": [    "dist"  ],  "scripts": {    "build": "rm -rf dist && rslib build && shx chmod +x dist/*.{js,cjs}",    "dev": "npx -y @modelcontextprotocol/inspector tsx src/index.ts"  }}

$ npx -y @modelcontextprotocol/inspector tsx src/index.tsStarting MCP inspector...New SSE connection
Spawned stdio transportConnected MCP client to backing server transportCreated web app transportSet up MCP proxy
🔍 MCP Inspector is up and running at http://localhost:5173 🚀

// src/server.tsexport const client: Pick<Client, 'callTool' | 'listTools' | 'close'> = {  callTool,  listTools,  close,};

#!/usr/bin/env node// src/index.tsimport { client as mcpBrowserClient } from "./server.js";
const server = new Server(  {    name: "example-servers/puppeteer",    version: "0.1.0",  },  {    capabilities: {      tools: {},    },  });// listToolsserver.setRequestHandler(ListToolsRequestSchema, mcpBrowserClient.listTools);// callToolserver.setRequestHandler(CallToolRequestSchema, async (request) =>  return await mcpBrowserClient.callTool(request.params););
async function runServer() {  const transport = new StdioServerTransport();  await server.connect(transport);}
runServer().catch(console.error);
process.stdin.on("close", () => {  console.error("Browser MCP Server closed");  server.close();});

import { z } from 'zod';
const toolsMap = {  browser_navigate: {    description: 'Navigate to a URL',    inputSchema: z.object({      url: z.string(),    }),    handle: async (args) => {      // Implements      const clickableElements = ['...']      return {        content: [          {            type: 'text',            text: `Navigated to ${args.url}\nclickable elements: ${clickableElements}`,          },        ],        isError: false,      }    }  },  browser_scroll: {    name: 'browser_scroll',    description: 'Scroll the page',    inputSchema: z.object({      amount: z        .number()        .describe('Pixels to scroll (positive for down, negative for up)'),    }),    handle: async (args) => {      return {        content: [          {            type: 'text',            text: `Scrolled ${actualScroll} pixels. ${              isAtBottom                ? 'Reached the bottom of the page.'                : 'Did not reach the bottom of the page.'            }`,          },        ],        isError: false,      };    }  },  // more};
const callTool = async ({ name, arguments: toolArgs }) => {  return handlers[name].handle(toolArgs);}

{    // Internal MCP Servers(in-process call)    fileSystem: {      name: 'fileSystem',      localClient: mcpFsClient,    },    commands: {      name: 'commands',      localClient: mcpCommandClient,    },    browser: {      name: 'browser',      localClient: mcpBrowserClient,    },
    // External MCP Servers(remote call)    fetch: {      command: 'uvx',      args: ['mcp-server-fetch'],    },    longbridge: {      command: 'longport-mcp',      args: [],      env: {}    }}

export type MCPServer<ServerNames extends string = string> = {  name: ServerNames;  status: 'activate' | 'error';  description?: string;  env?: Record<string, string>;+ /** in-process call, same as function call */+ localClient?: Pick<Client, 'callTool' | 'listTools' | 'close'>;  /** Stdio server */  command?: string;  args?: string[];};

import { MCPClient } from '@agent-infra/mcp-client';import { client as mcpBrowserClient } from '@agent-infra/mcp-server-browser';
 const client = new MCPClient([    {      name: 'browser',      description: 'web browser tools',      localClient: mcpBrowserClient,    }]);
const mcpTools = await client.listTools();
const response = await openai.chat.completions.create({  model,  messages,  // Different model vendors need to convert to the corresponding tools data format.  tools: convertToTools(tools),  tool_choice: 'auto',});

import asyncioimport openaiimport jsonfrom agents.mcp import MCPUtilfrom agents.mcp import MCPServerSse
from agents import set_tracing_disabled
set_tracing_disabled(True)
async def chat():    client = openai.AzureOpenAI(        azure_endpoint=base_url,        api_version=api_version,        api_key=ak,    )
+   async with MCPServerSse(+       name="fetch", params={"url": "https://{mcp_faas}.your_gateway/sse"}+   ) as mcp_server:+       tools = await MCPUtil.get_function_tools(+           mcp_server, convert_schemas_to_strict=False+       )        prompt = "请求下 https://agent-tars.com/，主要是做什么的？"        completion = client.chat.completions.create(            model=model_name,            messages=[{"role": "user", "content": prompt}],            max_tokens=max_tokens,+           tools=[+               {+                   "type": "function",+                   "function": {+                       "name": tool.name,+                       "description": tool.description,+                       "parameters": tool.params_json_schema,+                   },+               }+               for tool in tools+           ],        )
        for choice in completion.choices:            if isinstance(choice.message.tool_calls, list):                for tool_call in choice.message.tool_calls:                    if tool_call.function:+                       tool_result = await mcp_server.call_tool(+                           tool_name=tool_call.function.name,+                           arguments=json.loads(+                               tool_call.function.arguments+                           ), # or jsonrepair+                       )+                       print("tool_result", tool_result.content)
if __name__ == '__main__':    asyncio.run(chat())