feat: Clean up and translate comments to English

Co-authored-by: aider (gemini/gemini-2.5-pro) <[email protected]>

Author: Dango233

packages/mcp-server/src/bridge/bridge.ts

@@ -7,8 +7,8 @@ import {
   type Tool as GcliTool,
   type ToolResult,
   GeminiChat,
-  WebFetchTool, // <--- 添加这个导入
-  WebSearchTool, // <--- 添加这个导入
+  WebFetchTool,
+  WebSearchTool,
 } from '@google/gemini-cli-core';
 import {
   type CallToolResult,
@@ -27,7 +27,6 @@ export class GcliMcpBridge {
   private readonly config: Config;
   private readonly cliVersion: string;
   private readonly debugMode: boolean;
-  // **修改 2: 更新 transports 的类型以存储每个会话的 McpServer 和 Transport**
   private readonly sessions: Record<
     string,
     { mcpServer: McpServer; transport: StreamableHTTPServerTransport }
@@ -39,7 +38,6 @@ export class GcliMcpBridge {
     this.debugMode = debugMode;
   }
 
-  // **辅助方法：创建一个新的 McpServer 实例**
   private async createNewMcpServer(): Promise<McpServer> {
     const server = new McpServer(
       {
@@ -48,7 +46,6 @@ export class GcliMcpBridge {
       },
       { capabilities: { logging: {} } },
     );
-    // 在这里立即注册所有工具
     await this.registerAllGcliTools(server);
     return server;
   }
@@ -57,7 +54,6 @@ export class GcliMcpBridge {
     app.all('/mcp', async (req: Request, res: Response) => {
       const sessionId = req.headers['mcp-session-id'] as string | undefined;
 
-      // **修改 5: 从 `sessions` map 中获取会话对象**
       let session = sessionId ? this.sessions[sessionId] : undefined;
 
       if (!session) {
@@ -68,7 +64,6 @@ export class GcliMcpBridge {
           );
 
           try {
-            // **修改 6: 为新会话创建独立的 McpServer 和 Transport**
             const newMcpServer = await this.createNewMcpServer();
             const newTransport = new StreamableHTTPServerTransport({
               sessionIdGenerator: () => randomUUID(),
@@ -77,7 +72,6 @@ export class GcliMcpBridge {
                   this.debugMode,
                   `Session initialized: ${newSessionId}`,
                 );
-                // 存储新的会话对象
                 this.sessions[newSessionId] = {
                   mcpServer: newMcpServer,
                   transport: newTransport,
@@ -96,7 +90,6 @@ export class GcliMcpBridge {
               }
             };
 
-            // 将新的 transport 连接到新的 McpServer 实例
             await newMcpServer.connect(newTransport);
 
             session = { mcpServer: newMcpServer, transport: newTransport };
@@ -130,7 +123,6 @@ export class GcliMcpBridge {
       }
 
       try {
-        // **修改 7: 使用会话特定的 transport 来处理请求**
         await session.transport.handleRequest(req, res, req.body);
       } catch (e) {
         logger.error('Error handling request:', e);
@@ -150,33 +142,30 @@ export class GcliMcpBridge {
   }
 
   private registerGcliTool(tool: GcliTool, mcpServer: McpServer) {
-    let toolInstanceForExecution = tool; // 默认使用从 ToolRegistry 传入的原始工具实例
+    let toolInstanceForExecution = tool;
 
-    // 检查是否是需要特殊处理的网页工具
+    // For web tools, check if a custom model is specified via environment variable.
+    // If so, create a new tool instance with a proxied config to use that model.
     if (tool.name === 'google_web_search' || tool.name === 'web_fetch') {
       const toolModel = process.env.GEMINI_TOOLS_DEFAULT_MODEL;
 
-      // 如果为这些工具设置了专用的模型，则创建一个新的配置和工具实例
       if (toolModel) {
         logger.debug(
           this.debugMode,
           `Using custom model "${toolModel}" for tool "${tool.name}"`,
         );
 
-        // 步骤 1: 创建一个 this.config 的代理。
-        // 这个代理对象会拦截对 getModel 方法的调用。
+        // Create a proxy for this.config to override getModel.
         const proxyConfig = new Proxy(this.config, {
           get: (target, prop, receiver) => {
-            // 如果调用的方法是 getModel，则返回我们指定的工具模型
             if (prop === 'getModel') {
               return () => toolModel;
             }
-            // 对于所有其他属性和方法的调用，都代理到原始的 config 对象
             return Reflect.get(target, prop, receiver);
           },
         }) as Config;
 
-        // 步骤 2: 根据工具名称，使用这个代理配置来创建新的工具实例
+        // Create a new tool instance with the proxied config.
         if (tool.name === 'google_web_search') {
           toolInstanceForExecution = new WebSearchTool(proxyConfig);
         } else {
@@ -199,8 +188,7 @@ export class GcliMcpBridge {
         const startTime = Date.now();
         logger.info('MCP tool call started', { toolName: tool.name, args });
         try {
-          // *** 关键：现在所有工具都通过这个统一的路径执行 ***
-          // toolInstanceForExecution 要么是原始工具，要么是带有自定义模型配置的新实例
+          // toolInstanceForExecution is either the original tool or a new instance with a custom model config.
           const result = await toolInstanceForExecution.execute(
             args,
             extra.signal,
@@ -218,7 +206,8 @@ export class GcliMcpBridge {
             toolName: tool.name,
             durationMs,
           });
-          throw e; // 重新抛出错误，让 MCP SDK 处理
+          // Re-throw the error to be handled by the MCP SDK.
+          throw e;
         }
       },
     );
@@ -242,7 +231,7 @@ export class GcliMcpBridge {
         case 'boolean':
           return z.boolean().describe(prop.description || '');
         case 'array':
-          // This is the key fix: recursively call the converter for `items`.
+          // Recursively call the converter for `items`.
           if (!prop.items) {
             // A valid array schema MUST have `items`. Fallback to `any` if missing.
             return z.array(z.any()).describe(prop.description || '');
@@ -261,7 +250,6 @@ export class GcliMcpBridge {
       }
     };
 
-    // If no schema or properties, return an empty shape object.
     if (!jsonSchema || !jsonSchema.properties) {
       return {};
     }
@@ -275,7 +263,7 @@ export class GcliMcpBridge {
       }
       shape[key] = fieldSchema;
     }
-    return shape; // Directly return the shape object.
+    return shape;
   }
 
   private convertGcliResultToMcpResult(

packages/mcp-server/src/bridge/openai.ts

@@ -1,16 +1,16 @@
 import { Router, Request, Response } from 'express';
 import { type Config } from '@google/gemini-cli-core';
 import { createOpenAIStreamTransformer } from './stream-transformer.js';
-import { GeminiApiClient } from '../gemini-client.js'; // <-- 引入新类
-import { type OpenAIChatCompletionRequest } from '../types.js'; // <-- 引入新类型
+import { GeminiApiClient } from '../gemini-client.js';
+import { type OpenAIChatCompletionRequest } from '../types.js';
 import { mapErrorToOpenAIError } from '../utils/error-mapper.js';
 import { logger } from '../utils/logger.js';
 import { randomUUID } from 'node:crypto';
 
 export function createOpenAIRouter(config: Config, debugMode = false): Router {
   const router = Router();
 
-  // 中间件：为每个请求添加 requestId
+  // Middleware: Add a requestId to each request.
   router.use((req, res, next) => {
     (req as any).requestId = randomUUID();
     next();
@@ -31,23 +31,21 @@ export function createOpenAIRouter(config: Config, debugMode = false): Router {
       const stream = body.stream !== false;
 
       if (!stream) {
-        // 非流式响应逻辑可以稍后实现，或直接返回错误
+        // Non-streaming responses are not yet implemented.
         res
           .status(501)
           .json({ error: 'Non-streaming responses are not yet implemented.' });
         return;
       }
 
-      // --- 流式响应 ---
+      // --- Streaming Response ---
       res.setHeader('Content-Type', 'text/event-stream');
       res.setHeader('Cache-Control', 'no-cache');
       res.setHeader('Connection', 'keep-alive');
       res.flushHeaders();
 
-      // 1. 使用新的 GeminiApiClient
       const client = new GeminiApiClient(config, debugMode);
 
-      // 2. 发起请求，传递所有相关参数
       const geminiStream = await client.sendMessageStream({
         model: body.model,
         messages: body.messages,
@@ -57,8 +55,8 @@ export function createOpenAIRouter(config: Config, debugMode = false): Router {
 
       const openAIStream = createOpenAIStreamTransformer(body.model, debugMode);
 
-      // --- 修正的核心逻辑 ---
-      // 1. 创建一个 ReadableStream 来包装我们的 Gemini 事件流
+      // --- Core streaming logic ---
+      // Create a ReadableStream to wrap our Gemini event stream.
       const readableStream = new ReadableStream({
         async start(controller) {
           for await (const value of geminiStream) {
@@ -68,11 +66,11 @@ export function createOpenAIRouter(config: Config, debugMode = false): Router {
         },
       });
 
-      // 2. 将我们的流通过转换器
+      // Pipe our stream through the transformer.
       const transformedStream = readableStream.pipeThrough(openAIStream);
       const reader = transformedStream.getReader();
 
-      // 3. 手动读取每个转换后的块并立即写入响应
+      // Manually read each transformed chunk and write it to the response immediately.
       try {
         while (true) {
           const { done, value } = await reader.read();
@@ -84,7 +82,7 @@ export function createOpenAIRouter(config: Config, debugMode = false): Router {
       } finally {
         reader.releaseLock();
       }
-      // --- 修正结束 ---
+      // --- End of core streaming logic ---
 
       const durationMs = Date.now() - startTime;
       logger.info('OpenAI bridge request finished', {
@@ -103,20 +101,20 @@ export function createOpenAIRouter(config: Config, debugMode = false): Router {
       // 调用新的错误映射函数
       const { openAIError, statusCode } = mapErrorToOpenAIError(e);
 
-      // 使用映射后的状态码和错误对象进行响应
       if (!res.headersSent) {
         res.status(statusCode).json(openAIError);
       } else {
-        // 如果流已经开始，我们无法改变状态码，但可以在流中发送错误
+        // If headers are already sent, we can't change the status code,
+        // but we can send an error in the stream.
         res.write(`data: ${JSON.stringify({ error: openAIError.error })}\n\n`);
         res.end();
       }
     }
   });
 
-  // 可以添加 /v1/models 端点
+  // The /v1/models endpoint can be added here.
   router.get('/models', (req, res) => {
-    // 这里可以返回一个固定的模型列表，或者从 config 中获取
+    // This can return a fixed list of models or get them from the config.
     res.json({
       object: 'list',
       data: [

packages/mcp-server/src/bridge/stream-transformer.ts

@@ -1,7 +1,7 @@
 import { randomUUID } from 'node:crypto';
 import { type StreamChunk } from '../types.js';
 
-// --- OpenAI 响应结构接口 ---
+// --- OpenAI Response Interfaces ---
 interface OpenAIDelta {
   role?: 'assistant';
   content?: string | null;
@@ -28,7 +28,7 @@ interface OpenAIChunk {
   }[];
 }
 
-// --- 新的、有状态的转换器 ---
+// --- New Stateful Transformer ---
 export function createOpenAIStreamTransformer(
   model: string,
   debugMode = false,
@@ -89,13 +89,13 @@ export function createOpenAIStreamTransformer(
 
         case 'tool_code': {
           const { name, args } = chunk.data;
-          // **重要**: 在 ID 中嵌入函数名，以便在收到工具响应时可以解析它
+          // IMPORTANT: Embed the function name in the ID so it can be parsed when a tool response is received.
           const toolCallId = `call_${name}_${randomUUID()}`;
 
-          // OpenAI 流式工具调用需要分块发送
-          // 1. 发送包含函数名的块
+          // OpenAI streaming tool calls need to be sent in chunks.
+          // 1. Send the chunk containing the function name.
           const nameDelta: OpenAIDelta = {
-            ...delta, // 包含 role (如果是第一个块)
+            ...delta, // Include role if it's the first chunk
             tool_calls: [
               {
                 index: toolCallIndex,
@@ -107,7 +107,7 @@ export function createOpenAIStreamTransformer(
           };
           enqueueChunk(controller, createChunk(nameDelta));
 
-          // 2. 发送包含参数的块
+          // 2. Send the chunk containing the arguments.
           const argsDelta: OpenAIDelta = {
             tool_calls: [
               {
@@ -125,7 +125,7 @@ export function createOpenAIStreamTransformer(
         }
 
         case 'reasoning':
-          // 这些事件目前在 OpenAI 格式中没有直接对应项，可以选择忽略或以某种方式记录
+          // These events currently have no direct equivalent in the OpenAI format and can be ignored or logged.
           if (debugMode) {
             console.log(`[Stream Transformer] Ignoring chunk: ${chunk.type}`);
           }
@@ -134,7 +134,7 @@ export function createOpenAIStreamTransformer(
     },
 
     flush(controller) {
-      // 在流结束时，发送一个带有 `tool_calls` 或 `stop` 的 finish_reason
+      // At the end of the stream, send a finish_reason of 'tool_calls' or 'stop'.
       const finish_reason = toolCallIndex > 0 ? 'tool_calls' : 'stop';
       enqueueChunk(controller, createChunk({}, finish_reason));