mcp学习笔记（三）-Mcp传输协议代码示例

一、三种传输方式详解

1. Stdio 传输

1.1 原理说明

Stdio（标准输入输出）传输是最简单的传输方式。MCP Client 将Server 作为 子进程 启动，通过进程的 stdin 和 stdout 进行双向通信。

关键规则：

• 每条JSON-RPC 消息以 换行符 \n 分隔
• stdout 专用于协议消息，日志/调试信息必须输出到 stderr
• Client 与 Server 一对一绑定，生命周期同步

1.2 原理图(draw.io)

1.3 交互流程图(draw.io 时序图)

1.4 代码示例

Server 端： stdio_server.py

# main.py
from mcp.server.fastmcp import FastMCP
from mcp.server.fastmcp.prompts import base

import logging
import json

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

# 创建MCP实例
mcp = FastMCP(name="stdio-demo")


# 添加一个简单的资源
@mcp.tool()
def greeting(name: str = "World") -> str:
    """返回问候语"""
    return f"Hello, {name}!"


# 添加计算功能
@mcp.tool()
def add(a: int, b: int) -> int:
    """加法计算器"""
    return a + b


@mcp.resource("models://")
def get_models() -> str:
    """Get information about available AI models"""
    logger.info("Retrieving available models")
    models_data = [
        {
            "id": "gpt-4",
            "name": "GPT-4",
            "description": "OpenAI's GPT-4 large language model"
        },
        {
            "id": "llama-3-70b",
            "name": "LLaMA 3 (70B)",
            "description": "Meta's LLaMA 3 with 70 billion parameters"
        },
        {
            "id": "claude-3-sonnet",
            "name": "Claude 3 Sonnet",
            "description": "Anthropic's Claude 3 Sonnet model"
        }
    ]

    return json.dumps({"models": models_data})


# Define a greeting resource that dynamically constructs a personalized greeting
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Return a greeting for the given name

    Args:
        name: The name to greet

    Returns:
        A personalized greeting message
    """
    import urllib.parse
    # Decode URL-encoded name
    decoded_name = urllib.parse.unquote(name)
    logger.info(f"Generating greeting for {decoded_name}")
    return f"Hello, {decoded_name}!"


@mcp.resource("file://documents/{name}")
def read_document(name: str) -> str:
    """Read a document by name."""
    # This would normally read from disk
    return f"Content of {name}"


@mcp.prompt(title="Code Review")
def review_code(code: str) -> str:
    return f"Please review this code:\n\n{code}"


@mcp.prompt(title="Debug Assistant")
def debug_error(error: str) -> list[base.Message]:
    return [
        base.UserMessage("I'm seeing this error:"),
        base.UserMessage(error),
        base.AssistantMessage("I'll help debug that. What have you tried so far?"),
    ]


if __name__ == "__main__":
    mcp.run()

Client 端： stdio_client.py

# client.py
import sys
import urllib

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

import asyncio

import logging

import json

from mcp.types import TextContent, TextResourceContents

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


async def main():
    """Main client function that demonstrates MCP client features"""
    logger.info("Starting clean MCP client")

    try:
        logger.info("Connecting to server...")
        params = StdioServerParameters(
            command="python",  # Executable
            args=["sse-server.py"],  # Server script
            env=None,  # Optional environment variables
        )
        async with stdio_client(params) as (reader, writer):
            async with ClientSession(reader, writer) as session:
                logger.info("Initializing session")
                await session.initialize()

                # 1. Call the add tool
                logger.info("Testing calculator tool")
                add_result = await session.call_tool("add", arguments={"a": 5, "b": 7})
                if add_result and add_result.content:
                    text_content = next((content for content in add_result.content
                                         if isinstance(content, TextContent)), None)
                    if text_content:
                        print(f"\n1. Calculator result (5 + 7) = {text_content.text}")


                # 2. Get models resource
                logger.info("Testing models resource")
                models_response = await session.read_resource("models://")
                if models_response and models_response.contents:
                    text_resource = next((content for content in models_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        models = json.loads(text_resource.text)
                        print("\n3. Available models:")
                        for model in models.get("models", []):
                            print(f"   - {model['name']} ({model['id']}): {model['description']}")

                # 4. Get greeting resource
                logger.info("Testing greeting resource")
                name = "MCP Explorer"
                encoded_name = urllib.parse.quote(name)
                greeting_response = await session.read_resource(f"greeting://{encoded_name}")
                if greeting_response and greeting_response.contents:
                    text_resource = next((content for content in greeting_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        print(f"\n4. Greeting: {text_resource.text}")


                # 5. Get document resource
                logger.info("Testing document resource")
                document_response = await session.read_resource("file://documents/example.txt")
                if document_response and document_response.contents:
                    text_resource = next((content for content in document_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        print(f"\n5. Document content:")
                        print(f"   {text_resource.text}")

                # 6. Use code review prompt
                logger.info("Testing code review prompt")
                sample_code = "def hello_world():\n    print('Hello, world!')"
                prompt_response = await session.get_prompt("review_code", {"code": sample_code})
                if prompt_response and prompt_response.messages:
                    message = next((msg for msg in prompt_response.messages if msg.content), None)
                    if message and message.content:
                        text_content = next((content for content in [message.content]
                                             if isinstance(content, TextContent)), None)
                        if text_content:
                            print("\n6. Code review prompt:")
                            print(f"   {text_content.text}")

    except Exception:
        logger.exception("An error occurred")
        sys.exit(1)


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

---

2. SSE 传输

2.1 原理说明

SSE (Server-Sent Events) 传输基于 HTTP，使用 两个通道 实现双向通信：

通道	方向	方法	作用
/sse	Server → Client	GET	持久 SSE 长连接，服务器推送消息
/messages	Client → Server	POST	客户端发送 JSON-RPC 请求

⚠️ SSE 传输在MCP 协议 2025-03-26 版本中已被标记为 deprecated（弃用） ，推荐使用 Streamable HTTP 替代。

2.2 原理图 (draw.io)

2.3 交互流程图 (draw.io)

2.4 代码示例

Server 端： sse_server.py

# main.py
from mcp.server.fastmcp import FastMCP
from mcp.server.fastmcp.prompts import base

import logging
import json

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

# 创建MCP实例
mcp = FastMCP(name="sse-demo", port=8082)


# 添加一个简单的资源
@mcp.tool()
def greeting(name: str = "World") -> str:
    """返回问候语"""
    return f"Hello, {name}!"


# 添加计算功能
@mcp.tool()
def add(a: int, b: int) -> int:
    """加法计算器"""
    return a + b


@mcp.resource("models://")
def get_models() -> str:
    """Get information about available AI models"""
    logger.info("Retrieving available models")
    models_data = [
        {
            "id": "gpt-4",
            "name": "GPT-4",
            "description": "OpenAI's GPT-4 large language model"
        },
        {
            "id": "llama-3-70b",
            "name": "LLaMA 3 (70B)",
            "description": "Meta's LLaMA 3 with 70 billion parameters"
        },
        {
            "id": "claude-3-sonnet",
            "name": "Claude 3 Sonnet",
            "description": "Anthropic's Claude 3 Sonnet model"
        }
    ]

    return json.dumps({"models": models_data})


# Define a greeting resource that dynamically constructs a personalized greeting
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Return a greeting for the given name

    Args:
        name: The name to greet

    Returns:
        A personalized greeting message
    """
    import urllib.parse
    # Decode URL-encoded name
    decoded_name = urllib.parse.unquote(name)
    logger.info(f"Generating greeting for {decoded_name}")
    return f"Hello, {decoded_name}!"


@mcp.resource("file://documents/{name}")
def read_document(name: str) -> str:
    """Read a document by name."""
    # This would normally read from disk
    return f"Content of {name}"


@mcp.prompt(title="Code Review")
def review_code(code: str) -> str:
    return f"Please review this code:\n\n{code}"


@mcp.prompt(title="Debug Assistant")
def debug_error(error: str) -> list[base.Message]:
    return [
        base.UserMessage("I'm seeing this error:"),
        base.UserMessage(error),
        base.AssistantMessage("I'll help debug that. What have you tried so far?"),
    ]


if __name__ == "__main__":
    mcp.run(transport="sse")

Client 端： sse_client.py

# client.py
import sys
import urllib

from mcp.client.sse import sse_client
from mcp.client.session import ClientSession

import asyncio

import logging

import json

from mcp.types import TextContent, TextResourceContents

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


async def main():
    """Main client function that demonstrates MCP client features"""
    logger.info("Starting clean MCP client")

    try:
        logger.info("Connecting to server...")
        async with sse_client(url="http://localhost:8082/sse") as (reader, writer):
            async with ClientSession(reader, writer) as session:
                logger.info("Initializing session")
                await session.initialize()

                # 1. Call the add tool
                logger.info("Testing calculator tool")
                add_result = await session.call_tool("add", arguments={"a": 5, "b": 7})
                if add_result and add_result.content:
                    text_content = next((content for content in add_result.content
                                         if isinstance(content, TextContent)), None)
                    if text_content:
                        print(f"\n1. Calculator result (5 + 7) = {text_content.text}")


                # 2. Get models resource
                logger.info("Testing models resource")
                models_response = await session.read_resource("models://")
                if models_response and models_response.contents:
                    text_resource = next((content for content in models_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        models = json.loads(text_resource.text)
                        print("\n3. Available models:")
                        for model in models.get("models", []):
                            print(f"   - {model['name']} ({model['id']}): {model['description']}")

                # 4. Get greeting resource
                logger.info("Testing greeting resource")
                name = "MCP Explorer"
                encoded_name = urllib.parse.quote(name)
                greeting_response = await session.read_resource(f"greeting://{encoded_name}")
                if greeting_response and greeting_response.contents:
                    text_resource = next((content for content in greeting_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        print(f"\n4. Greeting: {text_resource.text}")


                # 5. Get document resource
                logger.info("Testing document resource")
                document_response = await session.read_resource("file://documents/example.txt")
                if document_response and document_response.contents:
                    text_resource = next((content for content in document_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        print(f"\n5. Document content:")
                        print(f"   {text_resource.text}")

                # 6. Use code review prompt
                logger.info("Testing code review prompt")
                sample_code = "def hello_world():\n    print('Hello, world!')"
                prompt_response = await session.get_prompt("review_code", {"code": sample_code})
                if prompt_response and prompt_response.messages:
                    message = next((msg for msg in prompt_response.messages if msg.content), None)
                    if message and message.content:
                        text_content = next((content for content in [message.content]
                                             if isinstance(content, TextContent)), None)
                        if text_content:
                            print("\n6. Code review prompt:")
                            print(f"   {text_content.text}")

    except Exception:
        logger.exception("An error occurred")
        sys.exit(1)


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

---

3. Streamable HTTP 传输

3.1 原理说明

Streamable HTTP 是MCP 协议 2025-03-26 版本引入的 新一代传输方式，作为 SSE 的替代方案。

核心设计：

特性	说明
单端点	所有通信通过POST /mcp 进行（路径可自定义）
灵活响应	服务器可返回 普通 JSON 或 SSE 流
会话管理	通过 Mcp-Session-Id 请求头管理，可选
有/无状态	同时支持有状态（会话绑定）和无状态（每次独立）模式
服务器推送	客户端可通过 GET /mcp 打开 SSE 流接收通知
会话终止	通过 DELETE /mcp 主动结束会话

3.2 原理图 (draw.io)

3.3 交互流程图 (draw.io)

3.4 代码示例

Server 端streamable_http_server.py

# main.py
from mcp.server.fastmcp import FastMCP
from mcp.server.fastmcp.prompts import base

import logging
import json

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

# 创建MCP实例
mcp = FastMCP(name="streamable-demo", port=8081, stateless_http=False)


# 添加一个简单的资源
@mcp.tool()
def greeting(name: str = "World") -> str:
    """返回问候语"""
    return f"Hello, {name}!"


# 添加计算功能
@mcp.tool()
def add(a: int, b: int) -> int:
    """加法计算器"""
    return a + b


@mcp.resource("models://")
def get_models() -> str:
    """Get information about available AI models"""
    logger.info("Retrieving available models")
    models_data = [
        {
            "id": "gpt-4",
            "name": "GPT-4",
            "description": "OpenAI's GPT-4 large language model"
        },
        {
            "id": "llama-3-70b",
            "name": "LLaMA 3 (70B)",
            "description": "Meta's LLaMA 3 with 70 billion parameters"
        },
        {
            "id": "claude-3-sonnet",
            "name": "Claude 3 Sonnet",
            "description": "Anthropic's Claude 3 Sonnet model"
        }
    ]

    return json.dumps({"models": models_data})


# Define a greeting resource that dynamically constructs a personalized greeting
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Return a greeting for the given name

    Args:
        name: The name to greet

    Returns:
        A personalized greeting message
    """
    import urllib.parse
    # Decode URL-encoded name
    decoded_name = urllib.parse.unquote(name)
    logger.info(f"Generating greeting for {decoded_name}")
    return f"Hello, {decoded_name}!"


@mcp.resource("file://documents/{name}")
def read_document(name: str) -> str:
    """Read a document by name."""
    # This would normally read from disk
    return f"Content of {name}"


@mcp.prompt(title="Code Review")
def review_code(code: str) -> str:
    return f"Please review this code:\n\n{code}"


@mcp.prompt(title="Debug Assistant")
def debug_error(error: str) -> list[base.Message]:
    return [
        base.UserMessage("I'm seeing this error:"),
        base.UserMessage(error),
        base.AssistantMessage("I'll help debug that. What have you tried so far?"),
    ]


if __name__ == "__main__":
    mcp.run(transport="streamable-http")

Client 端： streamable_http_client.py

# client.py - Streamable HTTP Protocol MCP Client
import sys
import urllib.parse
from mcp.client.streamable_http import streamablehttp_client

from mcp.client.session import ClientSession

import asyncio
import logging
import json

from mcp.types import TextContent, TextResourceContents
from mcp.server.fastmcp.prompts import base

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


async def main():
    """Main client function that demonstrates MCP client features with streamable protocol"""
    logger.info("Starting Streamable MCP Client")

    try:
        # Connect to streamable HTTP server
        logger.info("Connecting to streamable server at http://localhost:8081/mcp...")
        async with streamablehttp_client("http://localhost:8081/mcp") as (reader, writer, callback):
            async with ClientSession(reader, writer) as session:
                logger.info("Initializing session")
                await session.initialize()

                # 1. Call the greeting tool
                logger.info("Testing greeting tool")
                greeting_result = await session.call_tool("greeting", arguments={"name": "World"})
                if greeting_result and greeting_result.content:
                    text_content = next((content for content in greeting_result.content
                                         if isinstance(content, TextContent)), None)
                    if text_content:
                        print(f"\n1. Greeting: {text_content.text}")

                # 2. Call the add tool
                logger.info("Testing calculator tool")
                add_result = await session.call_tool("add", arguments={"a": 5, "b": 7})
                if add_result and add_result.content:
                    text_content = next((content for content in add_result.content
                                         if isinstance(content, TextContent)), None)
                    if text_content:
                        print(f"\n2. Calculator result (5 + 7) = {text_content.text}")

                # 3. Get models resource
                logger.info("Testing models resource")
                models_response = await session.read_resource("models://")
                if models_response and models_response.contents:
                    text_resource = next((content for content in models_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        models = json.loads(text_resource.text)
                        print("\n3. Available models:")
                        for model in models.get("models", []):
                            print(f"   - {model['name']} ({model['id']}): {model['description']}")

                # 4. Get greeting resource
                logger.info("Testing greeting resource")
                name = "MCP Explorer"
                encoded_name = urllib.parse.quote(name)
                greeting_response = await session.read_resource(f"greeting://{encoded_name}")
                if greeting_response and greeting_response.contents:
                    text_resource = next((content for content in greeting_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        print(f"\n4. Greeting: {text_resource.text}")

                # 5. Get document resource
                logger.info("Testing document resource")
                document_response = await session.read_resource("file://documents/example.txt")
                if document_response and document_response.contents:
                    text_resource = next((content for content in document_response.contents
                                          if isinstance(content, TextResourceContents)), None)
                    if text_resource:
                        print(f"\n5. Document content:")
                        print(f"   {text_resource.text}")

                # 6. Use code review prompt
                logger.info("Testing code review prompt")
                sample_code = "def hello_world():\n    print('Hello, world!')"
                prompt_response = await session.get_prompt("review_code", {"code": sample_code})
                if prompt_response and prompt_response.messages:
                    message = next((msg for msg in prompt_response.messages if msg.content), None)
                    if message and message.content:
                        text_content = next((content for content in [message.content]
                                             if isinstance(content, TextContent)), None)
                        if text_content:
                            print("\n6. Code review prompt:")
                            print(f"   {text_content.text}")

                # 7. Use debug error prompt (multi-message format)
                logger.info("Testing debug assistant prompt")
                error_message = "AttributeError: 'NoneType' object has no attribute 'method'"
                debug_response = await session.get_prompt("debug_error", {"error": error_message})
                if debug_response and debug_response.messages:
                    print("\n7. Debug assistant prompt (multi-message):")
                    for idx, msg in enumerate(debug_response.messages):
                        if isinstance(msg, base.UserMessage):
                            print(f"   [User Message {idx + 1}]: {msg.content.text if hasattr(msg.content, 'text') else msg.content}")
                        elif isinstance(msg, base.AssistantMessage):
                            print(f"   [Assistant Message {idx + 1}]: {msg.content.text if hasattr(msg.content, 'text') else msg.content}")

    except Exception:
        logger.exception("An error occurred")
        sys.exit(1)


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

---

二、三种传输方式对比

特性	Stdio	SSE	Streamable HTTP
协议版本	所有版本	2024-11-05 (已弃用)	2025-03-26 (推荐)
通信方式	进程 stdin/stdout	HTTP GET + POST (双端点)	统一 HTTP POST (单端点)
连接模式	进程绑定	持久 SSE 长连接	灵活 (可有状态/无状态)
网络支持	❌ 仅本地	✅ 支持	✅ 支持
端点数量	N/A	2 (/sse + /messages)	1 (/mcp)
流式响应	✅ 天然流式	✅ SSE 推送	✅ 可选 JSON/SSE 流
多客户端	❌ 一对一	✅	✅
无状态模式	❌	❌	✅
会话恢复	❌	❌ 重连困难	✅ 支持
基础设施兼容	N/A	⚠️ SSE 代理兼容问题	✅ 标准 HTTP
适合场景	本地 IDE / CLI	旧版网络服务	新项目首选