MCP-Chinese-Getting-Started-Guide

Model Context Protocol(MCP) 编程极速入门

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The Model Context Protocol (MCP) is an innovative open-source protocol that redefines the interaction between large language models (LLMs) and the external world. MCP provides a standardized approach for any large language model to easily connect to various data sources and tools, enabling seamless access and processing of information. MCP acts as a USB-C interface for AI applications, offering a standardized way for AI models to connect to different data sources and tools. The core functionalities of MCP include Resources, Prompts, Tools, Sampling, Roots, and Transports. This guide focuses on developing an MCP server for network search using Python and uv management. It covers initializing the project, installing dependencies, creating a server, implementing tool execution methods, and running the server. Additionally, it explains how to debug the MCP server using the Inspector tool, how to call tools from the server, and how to connect multiple MCP servers. The guide also introduces the Sampling feature, which allows pre- and post-tool execution operations, and demonstrates how to integrate MCP servers into LangChain for AI applications.

README:

Model Context Protocol(MCP) 编程极速入门

[TOC]

简介

模型上下文协议（MCP）是一个创新的开源协议，它重新定义了大语言模型（LLM）与外部世界的互动方式。MCP 提供了一种标准化方法，使任意大语音模型能够轻松连接各种数据源和工具，实现信息的无缝访问和处理。MCP 就像是 AI 应用程序的 USB-C 接口，为 AI 模型提供了一种标准化的方式来连接不同的数据源和工具。

MCP 有以下几个核心功能：

Resources 资源
Prompts 提示词
Tools 工具
Sampling 采样
Roots 根目录
Transports 传输层

因为大部分功能其实都是服务于 Claude 客户端的，本文更希望编写的 MCP 服务器服务与通用大语言模型，所以本文将会主要以“工具”为重点，其他功能会放到最后进行简单讲解。

其中 MCP 的传输层支持了 2 种协议的实现：stdio（标准输入/输出）和 SSE（服务器发送事件），因为 stdio 更为常用，所以本文会以 stdio 为例进行讲解。

本文将会使用 3.11 的 Python 版本，并使用 uv 来管理 Python 项目。同时代码将会在文末放到 Github 上，废话不多说，我们这就开始吧~

开发 MCP 服务器

在这一小节中，我们将会实现一个用于网络搜索的服务器。首先，我们先来通过 uv 初始化我们的项目。

uv 官方文档：https://docs.astral.sh/uv/

# 初始化项目
uv init mcp_getting_started
cd mcp_getting_started

# 创建虚拟环境并进入虚拟环境
uv venv
.venv\Scripts\activate.bat

# 安装依赖
uv add "mcp[cli]" httpx openai

然后我们来创建一个叫 web_search.py 文件，来实现我们的服务。MCP 为我们提供了2个对象：mcp.server.FastMCP 和 mcp.server.Server，mcp.server.FastMCP 是更高层的封装，我们这里就来使用它。

import httpx
from mcp.server import FastMCP

# # 初始化 FastMCP 服务器
app = FastMCP('web-search')

实现执行的方法非常简单，MCP 为我们提供了一个 @mcp.tool() 我们只需要将实现函数用这个装饰器装饰即可。函数名称将作为工具名称，参数将作为工具参数，并通过注释来描述工具与参数，以及返回值。

这里我们直接使用智普的接口，它这个接口不仅能帮我们搜索到相关的结果链接，并帮我们生成了对应链接中文章总结后的内容的，并且现阶段是免费的，非常适合我们。

官方文档：https://bigmodel.cn/dev/api/search-tool/web-search-pro

API Key 生成地址：https://bigmodel.cn/usercenter/proj-mgmt/apikeys

@app.tool()
async def web_search(query: str) -> str:
    """
    搜索互联网内容

    Args:
        query: 要搜索内容

    Returns:
        搜索结果的总结
    """

    async with httpx.AsyncClient() as client:
        response = await client.post(
            'https://open.bigmodel.cn/api/paas/v4/tools',
            headers={'Authorization': '换成你自己的API KEY'},
            json={
                'tool': 'web-search-pro',
                'messages': [
                    {'role': 'user', 'content': query}
                ],
                'stream': False
            }
        )

        res_data = []
        for choice in response.json()['choices']:
            for message in choice['message']['tool_calls']:
                search_results = message.get('search_result')
                if not search_results:
                    continue
                for result in search_results:
                    res_data.append(result['content'])

        return '\n\n\n'.join(res_data)

最后，我们来添加运行服务器的代码。

if __name__ == "__main__":
    app.run(transport='stdio')

调试 MCP 服务器

此时，我们就完成了 MCP 服务端的编写。下面，我们来使用官方提供的 Inspector 可视化工具来调试我们的服务器。

我们可以通过两种方法来运行Inspector：

请先确保已经安装了 node 环境。

通过 npx：

npx -y @modelcontextprotocol/inspector <command> <arg1> <arg2>

我们的这个代码运行命令为：

npx -y @modelcontextprotocol/inspector uv run web_search.py

通过 mcp dev 来运行：

mcp dev PYTHONFILE

我们的这个代码运行命令为：

mcp dev web_search.py

当出现如下提示则代表运行成功。

然后，我们打开这个地址，点击左侧的 Connect 按钮，即可连接我们刚写的服务。然后我们切换到 Tools 栏中，点击 List Tools 按钮即可看到我们刚写的工具，我们就可以开始进行调试啦。

开发 MCP 服务端

首先，我们先来看看如何在服务端如何调用我们刚才开发的 MCP 服务器中的工具。

import asyncio

from mcp.client.stdio import stdio_client
from mcp import ClientSession, StdioServerParameters

# 为 stdio 连接创建服务器参数
server_params = StdioServerParameters(
    # 服务器执行的命令，这里我们使用 uv 来运行 web_search.py
    command='uv',
    # 运行的参数
    args=['run', 'web_search.py'],
    # 环境变量，默认为 None，表示使用当前环境变量
    # env=None
)


async def main():
    # 创建 stdio 客户端
    async with stdio_client(server_params) as (stdio, write):
        # 创建 ClientSession 对象
        async with ClientSession(stdio, write) as session:
            # 初始化 ClientSession
            await session.initialize()

            # 列出可用的工具
            response = await session.list_tools()
            print(response)

            # 调用工具
            response = await session.call_tool('web_search', {'query': '今天杭州天气'})
            print(response)


if __name__ == '__main__':
    asyncio.run(main())

因为我们的python脚本需要在虚拟环境中才能运行，所以这里我们通过 uv 来启动我们的脚本。

下面我们来通过一个小例子来看看如何让 DeepSeek 来调用我们 MCP 服务器中的方法。

这里我们会用 dotenv 来管理我们相关的环境变量。.env 文件内容如下：

OPENAI_API_KEY=sk-89baxxxxxxxxxxxxxxxxxx
OPENAI_BASE_URL=https://api.deepseek.com
OPENAI_MODEL=deepseek-chat

首先我们来编写我们的 MCPClient 类。

import json
import asyncio
import os
from typing import Optional
from contextlib import AsyncExitStack

from openai import OpenAI
from dotenv import load_dotenv

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client


load_dotenv()


class MCPClient:
    def __init__(self):
        self.session: Optional[ClientSession] = None
        self.exit_stack = AsyncExitStack()
        self.client = OpenAI()

然后我们添加 connect_to_server 方法来初始化我们的 MCP 服务器的 session。

    async def connect_to_server(self):
        server_params = StdioServerParameters(
            command='uv',
            args=['run', 'web_search.py'],
            env=None
        )

        stdio_transport = await self.exit_stack.enter_async_context(
            stdio_client(server_params))
        stdio, write = stdio_transport
        self.session = await self.exit_stack.enter_async_context(
            ClientSession(stdio, write))

        await self.session.initialize()

然后我们再实现一个用于调用 MCP 服务器的方法来处理和 DeepSeek 之间的交互。

    async def process_query(self, query: str) -> str:
        # 这里需要通过 system prompt 来约束一下大语言模型，
        # 否则会出现不调用工具，自己乱回答的情况
        system_prompt = (
            "You are a helpful assistant."
            "You have the function of online search. "
            "Please MUST call web_search tool to search the Internet content before answering."
            "Please do not lose the user's question information when searching,"
            "and try to maintain the completeness of the question content as much as possible."
            "When there is a date related question in the user's question," 
            "please use the search function directly to search and PROHIBIT inserting specific time."
        )
        
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": query}
        ]

        # 获取所有 mcp 服务器 工具列表信息
        response = await self.session.list_tools()
        # 生成 function call 的描述信息
        available_tools = [{
            "type": "function",
            "function": {
                "name": tool.name,
                "description": tool.description,
                "input_schema": tool.inputSchema
            }
        } for tool in response.tools]

        # 请求 deepseek，function call 的描述信息通过 tools 参数传入
        response = self.client.chat.completions.create(
            model=os.getenv("OPENAI_MODEL"),
            messages=messages,
            tools=available_tools
        )

        # 处理返回的内容
        content = response.choices[0]
        if content.finish_reason == "tool_calls":
            # 如何是需要使用工具，就解析工具
            tool_call = content.message.tool_calls[0]
            tool_name = tool_call.function.name
            tool_args = json.loads(tool_call.function.arguments)

            # 执行工具
            result = await self.session.call_tool(tool_name, tool_args)
            print(f"\n\n[Calling tool {tool_name} with args {tool_args}]\n\n")
			
            # 将 deepseek 返回的调用哪个工具数据和工具执行完成后的数据都存入messages中
            messages.append(content.message.model_dump())
            messages.append({
                "role": "tool",
                "content": result.content[0].text,
                "tool_call_id": tool_call.id,
            })

            # 将上面的结果再返回给 deepseek 用于生产最终的结果
            response = self.client.chat.completions.create(
                model=os.getenv("OPENAI_MODEL"),
                messages=messages,
            )
            return response.choices[0].message.content

        return content.message.content

接着，我们来实现循环提问和最后退出后关闭session的操作。

    async def chat_loop(self):
        while True:
            try:
                query = input("\nQuery: ").strip()

                if query.lower() == 'quit':
                    break

                response = await self.process_query(query)
                print("\n" + response)

            except Exception as e:
                import traceback
                traceback.print_exc()

    async def cleanup(self):
        """Clean up resources"""
        await self.exit_stack.aclose()

最后，我们来完成运行这个客户端相关的代码

async def main():
    client = MCPClient()
    try:
        await client.connect_to_server()
        await client.chat_loop()
    finally:
        await client.cleanup()


if __name__ == "__main__":
    import sys

    asyncio.run(main())

这是一个最精简的代码，里面没有实现记录上下文消息等功能，只是为了用最简单的代码来了解如何通过大语言模型来调动 CMP 服务器。这里只演示了如何连接单服务器，如果你期望连接多个 MCP 服务器，无非就是循环一下 connect_to_server 中的代码，可以将他们封装成一个类，然后将所有的 MCP 服务器中的工具循环遍历生成一个大的 available_tools，然后在通过大语言模型的返回结果进行调用即可，这里就不再赘述了。

可以参考官方案例：https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/clients/simple-chatbot/mcp_simple_chatbot/main.py

Sampling 讲解

MCP 还为我们提供了一个 Sampling 的功能，这个如果从字面来理解会让人摸不到头脑，但实际上这个功能就给了我们一个在执行工具的前后的接口，我们可以在工具执行前后来执行一些操作。比如，当调用本地文件的删除的工具的时候，肯定是期望我们确认后再进行删除。那么，此时就可以使用这个功能。

下面我们就来实现这个人工监督的小功能。

首先，我们来创建个模拟拥有删除文件的 CMP 服务器：

# 服务端
from mcp.server import FastMCP
from mcp.types import SamplingMessage, TextContent

app = FastMCP('file_server')


@app.tool()
async def delete_file(file_path: str):
    # 创建 SamplingMessage 用于触发 sampling callback 函数
    result = await app.get_context().session.create_message(
        messages=[
            SamplingMessage(
                role='user', content=TextContent(
                    type='text', text=f'是否要删除文件: {file_path} (Y)')
            )
        ],
        max_tokens=100
    )

    # 获取到 sampling callback 函数的返回值，并根据返回值进行处理
    if result.content.text == 'Y':
        return f'文件 {file_path} 已被删除！！'


if __name__ == '__main__':
    app.run(transport='stdio')

这里最重要的就是需要通过create_message方法来创建一个 SamplingMessage 类型的 message，他会将这个 message 发送给 sampling callback 对应的函数中。

接着，我们来创建客户端的代码：

# 客户端
import asyncio

from mcp.client.stdio import stdio_client
from mcp import ClientSession, StdioServerParameters
from mcp.shared.context import RequestContext
from mcp.types import (
    TextContent,
    CreateMessageRequestParams,
    CreateMessageResult,
)

server_params = StdioServerParameters(
    command='uv',
    args=['run', 'file_server.py'],
)


async def sampling_callback(
        context: RequestContext[ClientSession, None],
        params: CreateMessageRequestParams,
):
    # 获取工具发送的消息并显示给用户
    input_message = input(params.messages[0].content.text)
    # 将用户输入发送回工具
    return CreateMessageResult(
        role='user',
        content=TextContent(
            type='text',
            text=input_message.strip().upper() or 'Y'
        ),
        model='user-input',
        stopReason='endTurn'
    )


async def main():
    async with stdio_client(server_params) as (stdio, write):
        async with ClientSession(
                stdio, write,
                # 设置 sampling_callback 对应的方法
                sampling_callback=sampling_callback
        ) as session:
            await session.initialize()
            res = await session.call_tool(
                'delete_file',
                {'file_path': 'C:/xxx.txt'}
            )
            # 获取工具最后执行完的返回结果
            print(res)


if __name__ == '__main__':
    asyncio.run(main())

特别要注意的是，目前在工具里面打印的内容实际上使用 stdio_client 是无法显示到命令行窗口的。所以，我们调试的话，可以使用 mcp.shared.memory.create_connected_server_and_client_session。

具体代码如下：

# 客户端
from mcp.shared.memory import (
    create_connected_server_and_client_session as create_session
)
# 这里需要引入服务端的 app 对象
from file_server import app

async def sampling_callback(context, params):
    ...

async def main():
    async with create_session(
        app._mcp_server,
        sampling_callback=sampling_callback
    ) as client_session:
        await client_session.call_tool(
            'delete_file', 
            {'file_path': 'C:/xxx.txt'}
        )

if __name__ == '__main__':
    asyncio.run(main())

Claude Desktop 加载 MCP Server

因为后面的两个功能实际上都是为了提高给 Claude 桌面端用的，所以这里先说下如何加载我们自定义的 MCP Server 到 Claude 桌面端。

首先，我们先打开配置。

我们点击 Developer 菜单，然后点击 Edit Config 按钮打开 Claude 桌面端的配置文件 claude_desktop_config.json

然后开始添加我们的服务器，服务器需要在 mcpServers 层级下，参数有 command、args、env。实际上，参数和 StdioServerParameters 对象初始化时候的参数是一样的。

{
  "mcpServers": {
    "web-search-server": {
      "command": "uv",
      "args": [
        "--directory",
        "D:/projects/mcp_getting_started",
        "run",
        "web_search.py"
      ]
    }
  }
}

最后，我们保存文件后重启 Claude 桌面端就可以在这里看到我们的插件了。

当然，我们也可以直接在我们插件的目录下运行以下命令来直接安装：

mcp install web_search.py

其他功能

Prompt

MCP 还为我们提供了一个生成 Prompt 模板的功能。他使用起来也很简单，只需要使用 prompt 装饰器装饰一下即可，代码如下：

from mcp.server import FastMCP

app = FastMCP('prompt_and_resources')

@app.prompt('翻译专家')
async def translate_expert(
        target_language: str = 'Chinese',
) -> str:
    return f'你是一个翻译专家，擅长将任何语言翻译成{target_language}。请翻译以下内容：'


if __name__ == '__main__':
    app.run(transport='stdio')

然后我们用上一节讲到的配置 Claude 桌面端 MCP 服务器的方法添加下我们的新 MCP 服务器。然后我们就可以点击右下角的图标开始使用啦。

他会让我们设置一下我们传入的参数，然后他会在我们的聊天窗口上生成一个附件。

Resource

我们还可以在 Claude 客户端上选择我们为用户提供的预设资源，同时也支持自定义的协议。具体代码如下：

from mcp.server import FastMCP

app = FastMCP('prompt_and_resources')

@app.resource('echo://static')
async def echo_resource():
    # 返回的是，当用户使用这个资源时，资源的内容
    return 'Echo!'

@app.resource('greeting://{name}')
async def get_greeting(name):
    return f'Hello, {name}!'


if __name__ == '__main__':
    app.run(transport='stdio')

然后，我们到 Claude 桌面端上看看。

这里要特别注意的是，目前 Claude 桌面端是没法读到资源装饰器设置 greeting://{name} 这种通配符的路径，未来将会被支持。但是，在我们的客户端代码中是可以当做资源模板来使用的，具体代码如下：

import asyncio
from pydantic import AnyUrl

from mcp.client.stdio import stdio_client
from mcp import ClientSession, StdioServerParameters

server_params = StdioServerParameters(
    command='uv',
    args=['run', 'prompt_and_resources.py'],
)


async def main():
    async with stdio_client(server_params) as (stdio, write):
        async with ClientSession(stdio, write) as session:
            await session.initialize()

            # 获取无通配符的资源列表
            res = await session.list_resources()
            print(res)

            # 获取有通配符的资源列表(资源模板)
            res = await session.list_resource_templates()
            print(res)

            # 读取资源，会匹配通配符
            res = await session.read_resource(AnyUrl('greeting://liming'))
            print(res)

            # 获取 Prompt 模板列表
            res = await session.list_prompts()
            print(res)

            # 使用 Prompt 模板
            res = await session.get_prompt(
                '翻译专家', arguments={'target_language': '英语'})
            print(res)


if __name__ == '__main__':
    asyncio.run(main())

在 LangChain 中使用 MCP 服务器

最近 LangChain 发布了一个新的开源项目 langchain-mcp-adapters，可以很方便的将 MCP 服务器集成到 LangChain 中。下面我们来看看如何使用它:

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

from langchain_mcp_adapters.tools import load_mcp_tools
from langgraph.prebuilt import create_react_agent

from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o")

server_params = StdioServerParameters(
    command='uv',
    args=['run', 'web_search.py'],
)

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()

        # 获取工具列表
        tools = await load_mcp_tools(session)

        # 创建并使用 ReAct agent
        agent = create_react_agent(model, tools)
        agent_response = await agent.ainvoke({'messages': '杭州今天天气怎么样？'})

更详细的使用方法请参考：https://github.com/langchain-ai/langchain-mcp-adapters

DeepSeek + cline + 自定义MCP = 图文大师

最后，我们来使用 VsCode 的 cline 插件，来通过 DeepSeek 和我们自定义的一个图片生成的 mcp 服务器来构建一个图文大师的应用。废话不多说，我们直接开始。

首先先来构建我们的图片生成的 mcp server，这里我们直接用 huggingface 上的 FLUX.1-schnell 模型，地址是：https://huggingface.co/spaces/black-forest-labs/FLUX.1-schnell 。这里我们不使用 gradio_client 库，而是会使用 httpx 手搓一个，因为使用 gradio_client 库可能会出现编码错误的bug。具体代码如下：

# image_server.py

import json
import httpx
from mcp.server import FastMCP


app = FastMCP('image_server')


@app.tool()
async def image_generation(image_prompt: str):
    """
    生成图片
    :param image_prompt: 图片描述，需要是英文
    :return: 图片保存到的本地路径
    """
    async with httpx.AsyncClient() as client:
        data = {'data': [image_prompt, 0, True, 512, 512, 3]}

        # 创建生成图片任务
        response1 = await client.post(
            'https://black-forest-labs-flux-1-schnell.hf.space/call/infer',
            json=data,
            headers={"Content-Type": "application/json"}
        )

        # 解析响应获取事件 ID
        response_data = response1.json()
        event_id = response_data.get('event_id')

        if not event_id:
            return '无法获取事件 ID'

        # 通过流式的方式拿到返回数据
        url = f'https://black-forest-labs-flux-1-schnell.hf.space/call/infer/{event_id}'
        full_response = ''
        async with client.stream('GET', url) as response2:
            async for chunk in response2.aiter_text():
                full_response += chunk

        return json.loads(full_response.split('data: ')[-1])[0]['url']

if __name__ == '__main__':
    app.run(transport='stdio')

然后我们可以在虚拟环境下使用下面的命令打开 MCP Inspector 进行调试下我们的工具。

mcp dev image_server.py

接着我们在 VsCode 中安装 cline 插件，当安装完插件后，我们配置一下我们的 deepseek 的 api key。接着，我们点击右上角的 MCP Server 按钮打开 mcp server 列表。

然后切换到 Installed Tab 点击 Configure MCP Servers 按钮来编辑自定义的 mcp 服务器。

配置如下：

{
  "mcpServers": {
    "image_server": {
      "command": "uv",
      "args": [
        "--directory",
        "D:/projects/mcp_getting_started",
        "run",
        "image_server.py"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

我们保存后，这里的这个小点是绿色的就表示我们的服务器已连接，然后我们就可以开始使用啦。

然后，我们就打开输入框，来输入我们的要写的文章的内容：

我们可以看到，他正确的调用了我们的工具

最后，就是可以看到生成的文章啦。

至此，整个 MCP 入门教程就到这里啦。相关代码会放到 github 仓库中：https://github.com/liaokongVFX/MCP-Chinese-Getting-Started-Guide

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