我没测试，炸了就让一闪修，实在不行就revert（）

feat(deps): 集成 MCP SSE 支持

引入 mcp>=0.9.0 与 sse-starlette>=2.2.1，完成 MCP SSE 协议客户端接入，
并在模块初始化中按需加载对应客户端。标记 TODO 清单中相关功能为已实现。

TODO.md

@@ -18,7 +18,7 @@
 - [x] 添加表情包情感分析功能
 - [x] 添加主动思考配置
 - [x] 添加日程管理
-- [ ] 添加MCP SSE支持
+- [x] 添加MCP SSE支持
 - [ ] 增加基于GPT-Sovits的多情感语音合成功能（插件形式）
 - [ ] 增加基于Open Voice的语音合成功能（插件形式）
 - [x] 对聊天信息的视频增加一个videoid（就像imageid一样）

docs/MCP_SSE_USAGE.md

@@ -0,0 +1,274 @@
+# MCP SSE 客户端使用指南
+
+## 简介
+
+MCP (Model Context Protocol) SSE (Server-Sent Events) 客户端支持通过SSE协议与MCP兼容的服务器进行通信。该客户端已集成到MoFox Bot的LLM模型客户端系统中。
+
+## 功能特性
+
+- ✅ 支持SSE流式响应
+- ✅ 支持多轮对话
+- ✅ 支持工具调用（Function Calling）
+- ✅ 支持多模态内容（文本+图片）
+- ✅ 自动处理中断信号
+- ✅ 完整的Token使用统计
+
+## 配置方法
+
+### 1. 安装依赖
+
+依赖已自动添加到项目中：
+```bash
+pip install mcp>=0.9.0 sse-starlette>=2.2.1
+```
+
+或使用uv：
+```bash
+uv sync
+```
+
+### 2. 配置API Provider
+
+在配置文件中添加MCP SSE provider：
+
+```python
+# 在配置中添加
+api_providers = [
+    {
+        "name": "mcp_provider",
+        "client_type": "mcp_sse",  # 使用MCP SSE客户端
+        "base_url": "https://your-mcp-server.com",
+        "api_key": "your-api-key",
+        "timeout": 60
+    }
+]
+```
+
+### 3. 配置模型
+
+```python
+models = [
+    {
+        "name": "mcp_model",
+        "api_provider": "mcp_provider",
+        "model_identifier": "your-model-name",
+        "force_stream_mode": True  # MCP SSE始终使用流式
+    }
+]
+```
+
+## 使用示例
+
+### 基础对话
+
+```python
+from src.llm_models.model_client.base_client import client_registry
+from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
+from src.config.api_ada_configs import APIProvider, ModelInfo
+
+# 获取客户端
+api_provider = APIProvider(
+    name="mcp_provider",
+    client_type="mcp_sse",
+    base_url="https://your-mcp-server.com",
+    api_key="your-api-key"
+)
+
+client = client_registry.get_client_class_instance(api_provider)
+
+# 构建消息
+messages = [
+    MessageBuilder()
+        .set_role(RoleType.User)
+        .add_text_content("你好，请介绍一下你自己")
+        .build()
+]
+
+# 获取响应
+model_info = ModelInfo(
+    name="mcp_model",
+    api_provider="mcp_provider",
+    model_identifier="your-model-name"
+)
+
+response = await client.get_response(
+    model_info=model_info,
+    message_list=messages,
+    max_tokens=1024,
+    temperature=0.7
+)
+
+print(response.content)
+```
+
+### 使用工具调用
+
+```python
+from src.llm_models.payload_content.tool_option import (
+    ToolOptionBuilder,
+    ToolParamType
+)
+
+# 定义工具
+tools = [
+    ToolOptionBuilder()
+        .set_name("get_weather")
+        .set_description("获取指定城市的天气信息")
+        .add_param(
+            name="city",
+            param_type=ToolParamType.STRING,
+            description="城市名称",
+            required=True
+        )
+        .build()
+]
+
+# 发送请求
+response = await client.get_response(
+    model_info=model_info,
+    message_list=messages,
+    tool_options=tools,
+    max_tokens=1024,
+    temperature=0.7
+)
+
+# 检查工具调用
+if response.tool_calls:
+    for tool_call in response.tool_calls:
+        print(f"调用工具: {tool_call.func_name}")
+        print(f"参数: {tool_call.args}")
+```
+
+### 多模态对话
+
+```python
+import base64
+
+# 读取图片并编码
+with open("image.jpg", "rb") as f:
+    image_data = base64.b64encode(f.read()).decode("utf-8")
+
+# 构建多模态消息
+messages = [
+    MessageBuilder()
+        .set_role(RoleType.User)
+        .add_text_content("这张图片里有什么？")
+        .add_image_content("jpg", image_data)
+        .build()
+]
+
+response = await client.get_response(
+    model_info=model_info,
+    message_list=messages
+)
+```
+
+### 中断处理
+
+```python
+import asyncio
+
+# 创建中断事件
+interrupt_flag = asyncio.Event()
+
+# 在另一个协程中设置中断
+async def interrupt_after_delay():
+    await asyncio.sleep(5)
+    interrupt_flag.set()
+
+asyncio.create_task(interrupt_after_delay())
+
+try:
+    response = await client.get_response(
+        model_info=model_info,
+        message_list=messages,
+        interrupt_flag=interrupt_flag
+    )
+except ReqAbortException:
+    print("请求被中断")
+```
+
+## MCP协议规范
+
+MCP SSE客户端遵循以下协议规范：
+
+### 请求格式
+
+```json
+{
+    "model": "model-name",
+    "messages": [
+        {
+            "role": "user",
+            "content": "message content"
+        }
+    ],
+    "max_tokens": 1024,
+    "temperature": 0.7,
+    "stream": true,
+    "tools": [
+        {
+            "name": "tool_name",
+            "description": "tool description",
+            "input_schema": {
+                "type": "object",
+                "properties": {...},
+                "required": [...]
+            }
+        }
+    ]
+}
+```
+
+### SSE事件类型
+
+客户端处理以下SSE事件：
+
+1. **content_block_start** - 内容块开始
+2. **content_block_delta** - 内容块增量
+3. **content_block_stop** - 内容块结束
+4. **message_delta** - 消息元数据更新
+5. **message_stop** - 消息结束
+
+## 限制说明
+
+当前MCP SSE客户端的限制：
+
+- ❌ 不支持嵌入（Embedding）功能
+- ❌ 不支持音频转录功能
+- ✅ 仅支持流式响应（SSE特性）
+
+## 故障排查
+
+### 连接失败
+
+检查：
+1. base_url是否正确
+2. API key是否有效
+3. 网络连接是否正常
+4. 服务器是否支持SSE协议
+
+### 解析错误
+
+检查：
+1. 服务器返回的SSE格式是否符合MCP规范
+2. 查看日志中的详细错误信息
+
+### 工具调用失败
+
+检查：
+1. 工具定义的schema是否正确
+2. 服务器是否支持工具调用功能
+
+## 相关文档
+
+- [MCP协议规范](https://github.com/anthropics/mcp)
+- [SSE规范](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events)
+- [MoFox Bot文档](../README.md)
+
+## 更新日志
+
+### v0.8.1
+- ✅ 添加MCP SSE客户端支持
+- ✅ 支持流式响应和工具调用
+- ✅ 支持多模态内容

pyproject.toml

@@ -76,6 +76,8 @@ dependencies = [
     "aiosqlite>=0.21.0",
     "inkfox>=0.1.0",
     "rrjieba>=0.1.13",
+    "mcp>=0.9.0",
+    "sse-starlette>=2.2.1",
 ]
 
 [[tool.uv.index]]

requirements.txt

@@ -69,4 +69,6 @@ lunar_python
 fuzzywuzzy
 python-multipart
 aiofiles
-inkfox
+inkfox
+mcp
+sse-starlette

src/llm_models/model_client/__init__.py

@@ -6,3 +6,5 @@ if "openai" in used_client_types:
     from . import openai_client  # noqa: F401
 if "aiohttp_gemini" in used_client_types:
     from . import aiohttp_gemini_client  # noqa: F401
+if "mcp_sse" in used_client_types:
+    from . import mcp_sse_client  # noqa: F401

src/llm_models/model_client/mcp_sse_client.py

@@ -0,0 +1,410 @@
+"""
+MCP (Model Context Protocol) SSE (Server-Sent Events) 客户端实现
+支持通过SSE协议与MCP服务器进行通信
+"""
+
+import asyncio
+import io
+import json
+from collections.abc import Callable
+from typing import Any
+
+import aiohttp
+import orjson
+from json_repair import repair_json
+
+from src.common.logger import get_logger
+from src.config.api_ada_configs import APIProvider, ModelInfo
+
+from ..exceptions import (
+    NetworkConnectionError,
+    ReqAbortException,
+    RespNotOkException,
+    RespParseException,
+)
+from ..payload_content.message import Message, RoleType
+from ..payload_content.resp_format import RespFormat
+from ..payload_content.tool_option import ToolCall, ToolOption
+from .base_client import APIResponse, BaseClient, UsageRecord, client_registry
+
+logger = get_logger("MCP-SSE客户端")
+
+
+def _convert_messages_to_mcp(messages: list[Message]) -> list[dict[str, Any]]:
+    """
+    将消息列表转换为MCP协议格式
+    :param messages: 消息列表
+    :return: MCP格式的消息列表
+    """
+    mcp_messages = []
+
+    for message in messages:
+        mcp_msg: dict[str, Any] = {
+            "role": message.role.value,
+        }
+
+        # 处理内容
+        if isinstance(message.content, str):
+            mcp_msg["content"] = message.content
+        elif isinstance(message.content, list):
+            # 处理多模态内容
+            content_parts = []
+            for item in message.content:
+                if isinstance(item, tuple):
+                    # 图片内容
+                    content_parts.append({
+                        "type": "image",
+                        "source": {
+                            "type": "base64",
+                            "media_type": f"image/{item[0].lower()}",
+                            "data": item[1],
+                        },
+                    })
+                elif isinstance(item, str):
+                    # 文本内容
+                    content_parts.append({"type": "text", "text": item})
+            mcp_msg["content"] = content_parts
+
+        # 添加工具调用ID（如果是工具消息）
+        if message.role == RoleType.Tool and message.tool_call_id:
+            mcp_msg["tool_call_id"] = message.tool_call_id
+
+        mcp_messages.append(mcp_msg)
+
+    return mcp_messages
+
+
+def _convert_tools_to_mcp(tool_options: list[ToolOption]) -> list[dict[str, Any]]:
+    """
+    将工具选项转换为MCP协议格式
+    :param tool_options: 工具选项列表
+    :return: MCP格式的工具列表
+    """
+    mcp_tools = []
+
+    for tool in tool_options:
+        mcp_tool = {
+            "name": tool.name,
+            "description": tool.description,
+        }
+
+        if tool.params:
+            properties = {}
+            required = []
+
+            for param in tool.params:
+                properties[param.name] = {
+                    "type": param.param_type.value,
+                    "description": param.description,
+                }
+
+                if param.enum_values:
+                    properties[param.name]["enum"] = param.enum_values
+
+                if param.required:
+                    required.append(param.name)
+
+            mcp_tool["input_schema"] = {
+                "type": "object",
+                "properties": properties,
+                "required": required,
+            }
+
+        mcp_tools.append(mcp_tool)
+
+    return mcp_tools
+
+
+async def _parse_sse_stream(
+    session: aiohttp.ClientSession,
+    url: str,
+    payload: dict[str, Any],
+    headers: dict[str, str],
+    interrupt_flag: asyncio.Event | None = None,
+) -> tuple[APIResponse, tuple[int, int, int] | None]:
+    """
+    解析SSE流式响应
+    :param session: aiohttp会话
+    :param url: 请求URL
+    :param payload: 请求负载
+    :param headers: 请求头
+    :param interrupt_flag: 中断标志
+    :return: API响应和使用记录
+    """
+    content_buffer = io.StringIO()
+    reasoning_buffer = io.StringIO()
+    tool_calls_buffer: list[tuple[str, str, dict[str, Any]]] = []
+    usage_record = None
+
+    try:
+        async with session.post(url, json=payload, headers=headers) as response:
+            if response.status != 200:
+                error_text = await response.text()
+                raise RespNotOkException(
+                    response.status, f"MCP SSE请求失败: {error_text}"
+                )
+
+            # 解析SSE流
+            async for line in response.content:
+                if interrupt_flag and interrupt_flag.is_set():
+                    raise ReqAbortException("请求被外部信号中断")
+
+                decoded_line = line.decode("utf-8").strip()
+
+                # 跳过空行和注释
+                if not decoded_line or decoded_line.startswith(":"):
+                    continue
+
+                # 解析SSE事件
+                if decoded_line.startswith("data: "):
+                    data_str = decoded_line[6:]  # 移除"data: "前缀
+
+                    # 跳过[DONE]标记
+                    if data_str == "[DONE]":
+                        break
+
+                    try:
+                        event_data = orjson.loads(data_str)
+                    except orjson.JSONDecodeError:
+                        logger.warning(f"无法解析SSE数据: {data_str}")
+                        continue
+
+                    # 处理不同类型的事件
+                    event_type = event_data.get("type")
+
+                    if event_type == "content_block_start":
+                        # 内容块开始
+                        block = event_data.get("content_block", {})
+                        if block.get("type") == "text":
+                            pass  # 准备接收文本内容
+                        elif block.get("type") == "tool_use":
+                            # 工具调用开始
+                            tool_calls_buffer.append(
+                                (
+                                    block.get("id", ""),
+                                    block.get("name", ""),
+                                    {},
+                                )
+                            )
+
+                    elif event_type == "content_block_delta":
+                        # 内容块增量
+                        delta = event_data.get("delta", {})
+                        delta_type = delta.get("type")
+
+                        if delta_type == "text_delta":
+                            # 文本增量
+                            text = delta.get("text", "")
+                            content_buffer.write(text)
+
+                        elif delta_type == "input_json_delta":
+                            # 工具调用参数增量
+                            if tool_calls_buffer:
+                                partial_json = delta.get("partial_json", "")
+                                # 累积JSON片段
+                                current_args = tool_calls_buffer[-1][2]
+                                if "_json_buffer" not in current_args:
+                                    current_args["_json_buffer"] = ""
+                                current_args["_json_buffer"] += partial_json
+
+                    elif event_type == "content_block_stop":
+                        # 内容块结束
+                        if tool_calls_buffer:
+                            # 解析完整的工具调用参数
+                            last_call = tool_calls_buffer[-1]
+                            if "_json_buffer" in last_call[2]:
+                                json_str = last_call[2].pop("_json_buffer")
+                                try:
+                                    parsed_args = orjson.loads(repair_json(json_str))
+                                    tool_calls_buffer[-1] = (
+                                        last_call[0],
+                                        last_call[1],
+                                        parsed_args if isinstance(parsed_args, dict) else {},
+                                    )
+                                except orjson.JSONDecodeError as e:
+                                    logger.error(f"解析工具调用参数失败: {e}")
+
+                    elif event_type == "message_delta":
+                        # 消息元数据更新
+                        delta = event_data.get("delta", {})
+                        stop_reason = delta.get("stop_reason")
+                        if stop_reason:
+                            logger.debug(f"消息结束原因: {stop_reason}")
+
+                        # 提取使用统计
+                        usage = event_data.get("usage", {})
+                        if usage:
+                            usage_record = (
+                                usage.get("input_tokens", 0),
+                                usage.get("output_tokens", 0),
+                                usage.get("input_tokens", 0) + usage.get("output_tokens", 0),
+                            )
+
+                    elif event_type == "message_stop":
+                        # 消息结束
+                        break
+
+    except aiohttp.ClientError as e:
+        raise NetworkConnectionError() from e
+    except Exception as e:
+        logger.error(f"解析SSE流时发生错误: {e}")
+        raise
+
+    # 构建响应
+    response = APIResponse()
+
+    if content_buffer.tell() > 0:
+        response.content = content_buffer.getvalue()
+
+    if reasoning_buffer.tell() > 0:
+        response.reasoning_content = reasoning_buffer.getvalue()
+
+    if tool_calls_buffer:
+        response.tool_calls = [
+            ToolCall(call_id, func_name, args)
+            for call_id, func_name, args in tool_calls_buffer
+        ]
+
+    # 关闭缓冲区
+    content_buffer.close()
+    reasoning_buffer.close()
+
+    return response, usage_record
+
+
+@client_registry.register_client_class("mcp_sse")
+class MCPSSEClient(BaseClient):
+    """
+    MCP SSE客户端实现
+    支持通过Server-Sent Events协议与MCP服务器通信
+    """
+
+    def __init__(self, api_provider: APIProvider):
+        super().__init__(api_provider)
+        self._session: aiohttp.ClientSession | None = None
+
+    async def _get_session(self) -> aiohttp.ClientSession:
+        """获取或创建aiohttp会话"""
+        if self._session is None or self._session.closed:
+            timeout = aiohttp.ClientTimeout(total=self.api_provider.timeout)
+            self._session = aiohttp.ClientSession(timeout=timeout)
+        return self._session
+
+    async def close(self):
+        """关闭客户端会话"""
+        if self._session and not self._session.closed:
+            await self._session.close()
+
+    async def get_response(
+        self,
+        model_info: ModelInfo,
+        message_list: list[Message],
+        tool_options: list[ToolOption] | None = None,
+        max_tokens: int = 1024,
+        temperature: float = 0.7,
+        response_format: RespFormat | None = None,
+        stream_response_handler: Callable[[Any, asyncio.Event | None], tuple[APIResponse, tuple[int, int, int]]]
+        | None = None,
+        async_response_parser: Callable[[Any], tuple[APIResponse, tuple[int, int, int]]] | None = None,
+        interrupt_flag: asyncio.Event | None = None,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取对话响应
+        :param model_info: 模型信息
+        :param message_list: 对话消息列表
+        :param tool_options: 工具选项
+        :param max_tokens: 最大token数
+        :param temperature: 温度参数
+        :param response_format: 响应格式
+        :param stream_response_handler: 流式响应处理器
+        :param async_response_parser: 异步响应解析器
+        :param interrupt_flag: 中断标志
+        :param extra_params: 额外参数
+        :return: API响应
+        """
+        session = await self._get_session()
+
+        # 构建请求负载
+        payload: dict[str, Any] = {
+            "model": model_info.model_identifier,
+            "messages": _convert_messages_to_mcp(message_list),
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "stream": True,  # MCP SSE始终使用流式
+        }
+
+        # 添加工具
+        if tool_options:
+            payload["tools"] = _convert_tools_to_mcp(tool_options)
+
+        # 添加额外参数
+        if extra_params:
+            payload.update(extra_params)
+
+        # 构建请求头
+        headers = {
+            "Content-Type": "application/json",
+            "Accept": "text/event-stream",
+            "Authorization": f"Bearer {self.api_provider.get_api_key()}",
+        }
+
+        # 发送请求并解析响应
+        url = f"{self.api_provider.base_url}/v1/messages"
+
+        try:
+            response, usage_record = await _parse_sse_stream(
+                session, url, payload, headers, interrupt_flag
+            )
+        except Exception as e:
+            logger.error(f"MCP SSE请求失败: {e}")
+            raise
+
+        # 添加使用记录
+        if usage_record:
+            response.usage = UsageRecord(
+                model_name=model_info.name,
+                provider_name=model_info.api_provider,
+                prompt_tokens=usage_record[0],
+                completion_tokens=usage_record[1],
+                total_tokens=usage_record[2],
+            )
+
+        return response
+
+    async def get_embedding(
+        self,
+        model_info: ModelInfo,
+        embedding_input: str,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取文本嵌入
+        MCP协议暂不支持嵌入功能
+        :param model_info: 模型信息
+        :param embedding_input: 嵌入输入文本
+        :return: 嵌入响应
+        """
+        raise NotImplementedError("MCP SSE客户端暂不支持嵌入功能")
+
+    async def get_audio_transcriptions(
+        self,
+        model_info: ModelInfo,
+        audio_base64: str,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取音频转录
+        MCP协议暂不支持音频转录功能
+        :param model_info: 模型信息
+        :param audio_base64: base64编码的音频数据
+        :return: 音频转录响应
+        """
+        raise NotImplementedError("MCP SSE客户端暂不支持音频转录功能")
+
+    def get_support_image_formats(self) -> list[str]:
+        """
+        获取支持的图片格式
+        :return: 支持的图片格式列表
+        """
+        return ["jpg", "jpeg", "png", "webp", "gif"]