Model Context Protocol (MCP): STDIO vs. SSE模型上下文協定 (MCP)：STDIO vs. SSE

The Model Context Protocol (MCP) is revolutionizing how AI applications interact with various tools and services. By providing a unified API, MCP simplifies the integration of Large Language Models (LLMs) with everything from internet search to GitHub, Slack, and Docker. As you delve into building or using MCP-enabled systems, you’ll encounter two primary methods for this interaction: Standard Input/Output (STDIO) and Server-Sent Events (SSE).
模型上下文協定 (MCP) 正在徹底改變 AI 應用程式與各種工具服務的互動方式。透過提供統一 API，MCP 簡化了大型語言模型 (LLM) 與網路搜尋、GitHub、Slack、Docker 等服務的整合。當你開發或使用 MCP 系統時，會遇到兩種主要互動方式：標準輸入/輸出 (STDIO) 和伺服器推送事件 (SSE)。

Understanding the difference between these two is crucial for choosing the right approach for your AI project. Let’s break them down.
理解這兩者的差異對於選擇適合 AI 專案的方法至關重要。讓我們來深入解析。

What is MCP, Briefly?

Before diving into STDIO and SSE, let’s quickly recap what MCP does. Imagine you want your AI agent to use a specific tool, like a code interpreter or a database query tool.
在探討 STDIO 和 SSE 之前，先快速回顧 MCP 的功能。假設你想讓 AI 代理程式使用特定工具，例如程式碼直譯器或資料庫查詢工具。

• Without MCP: You’d need to write custom, often complex, code for each tool integration.
  沒有 MCP 的情況：你需要為每個工具整合編寫複雜的自訂程式碼。
• With MCP: It acts as a standardized bridge. Your LLM (via an MCP client) can communicate with tools (hosted in an MCP server or script) using a common protocol, making tool integration much smoother.
  使用 MCP：它就像標準化橋樑。你的 LLM（透過 MCP 客戶端）能使用通用協定與工具（部署於 MCP 伺服器或腳本）溝通，大幅簡化工具整合。

If you’re interested in a broader look at integrating MCP servers, especially with frameworks like OpenAI Agents, you might find this useful: Model Context Protocol (MCP) Servers with the OpenAI Agents.
若想深入了解 MCP 伺服器整合（特別是與 OpenAI Agents 等框架），這篇可能對你有幫助：Model Context Protocol (MCP) Servers with the OpenAI Agents。

STDIO (Standard Input/Output) in MCP
MCP 中的 STDIO（標準輸入/輸出）

What it is:
STDIO in the context of MCP refers to using standard input and standard output streams — the same way you interact with command-line applications in your terminal. If you can run a script or an application from your terminal (e.g., python my_tool.py or npx my-cli-tool), you can potentially wrap it into an MCP service using the STDIO method.
MCP 中的 STDIO 是指使用標準輸入/輸出串流——就像在終端機操作命令列應用程式。如果你能從終端執行腳本或應用（例如 python my_tool.py 或 npx my-cli-tool），就能用 STDIO 方法將其封裝成 MCP 服務。

How it works:

1. User Interaction: A user asks the LLM a question.
   使用者互動：使用者向 LLM 提問。
2. MCP Client: The LLM, through an MCP client, determines a tool is needed.
   MCP 客戶端：LLM 透過 MCP 客戶端判斷需要工具支援。
3. Local Execution: The MCP client executes a local script (Python, Bash, etc.) that contains the tool’s logic. This script reads input from stdin and writes its output to stdout.
   本地執行：MCP 客戶端執行包含工具邏輯的本地腳本（Python、Bash 等）。該腳本從 stdin 讀取輸入，並將輸出寫入 stdout。
4. Response to LLM: The output from the script is captured and sent back to the LLM as context.
   回傳 LLM：腳本輸出被擷取後，作為上下文送回 LLM。
5. Answer to User: The LLM uses this context to formulate an answer for the user.
   回覆使用者：LLM 利用此上下文生成最終答案。

Key Characteristics of STDIO:
STDIO 主要特性：

• Local Execution: Runs directly on your local machine.
  本地執行：直接在本地機器運作。
• Terminal-Based: Leverages command-line executables and scripts.
  終端導向：利用命令列可執行檔與腳本。
• Simplicity for Local Tools: Great for integrating existing local scripts or tools without needing web server infrastructure.
  本地工具簡易性：無需網頁伺服器架構即可整合現有本地腳本或工具。

For a practical guide on setting up an MCP server using the STDIO method, check out: How to Build an MCP Server for AI (used stdio).
實作 STDIO 方法建立 MCP 伺服器的實用指南：How to Build an MCP Server for AI (used stdio)。

SSE (Server-Sent Events) in MCP
MCP 中的 SSE（伺服器推送事件）

What it is:
SSE in MCP involves setting up a lightweight web server that tools can connect to. This method uses Server-Sent Events, a web technology that allows a server to push updates to a client over a single HTTP connection. Think of it as a mini web application dedicated to serving your AI tools.
MCP 中的 SSE 需建立輕量級網頁伺服器供工具連接。此方法採用伺服器推送事件技術——允許伺服器透過單一 HTTP 連線主動推送更新至客戶端。可以把它想像成專為 AI 工具設計的微型網頁應用程式。

How it works:

1. User Interaction: A user asks the LLM a question.
   使用者互動：使用者向 LLM 提問。
2. MCP Client: The LLM, via an MCP client (like Cursor or a custom integration), connects to an MCP server.
   MCP 客戶端：LLM 透過 MCP 客戶端（如 Cursor 或自訂整合）連接 MCP 伺服器。
3. MCP Server Interaction: The MCP client sends a request to the MCP server (which can be running locally, e.g., http://localhost:3000, or on a public web server). The server hosts the tool(s).
   MCP 伺服器互動：MCP 客戶端向伺服器發送請求（可運行於本地如 http://localhost:3000 或公開網頁伺服器）。伺服器託管工具。
4. Tool Execution & Response: The MCP server executes the relevant tool and streams the response back to the MCP client using SSE.
   工具執行與回應：MCP 伺服器執行相關工具，並用 SSE 串流回傳結果至客戶端。
5. Context to LLM: The MCP client provides this response to the LLM as context.
   提供上下文給 LLM：MCP 客戶端將此回應作為上下文提供給 LLM。
6. Answer to User: The LLM formulates the final answer.
   回覆使用者：LLM 生成最終答案。

Key Characteristics of SSE:
SSE 主要特性：

• Web-Based: Operates like a web application/service.
  網頁架構：運作方式類似網頁應用/服務。
• Network Accessible: Can be run locally or deployed to the web, making tools accessible over a network.
  網路可訪問性：可在本地運行或部署至網路，使工具能透過網路存取。
• Scalability & Sharing: Better suited for tools that need to be accessed by multiple clients or from different machines, or if you plan to publish your tool online.
  擴展性與共享性：更適合需多客戶端存取、跨裝置使用或線上發布的工具。

Which Method Should You Choose?
該選擇哪種方法？

The choice between STDIO and SSE largely depends on your specific needs:
STDIO 與 SSE 的選擇主要取決於需求：

Choose STDIO if:

• You’re working with tools that are already command-line scripts running locally.
  你正在使用既有的本地命令列腳本工具。
• You need a quick way to integrate a personal tool for your own use.
  需要快速整合個人自用工具。
• You’re primarily developing and testing locally.
  主要在本地開發測試階段。

Choose SSE if:

• You want to create a tool that can be accessed by others or by different applications over a network.
  你想建立可供他人或不同應用程式透過網路存取的工具。
• You plan to deploy your AI tool as part of a web service.
  計劃將 AI 工具部署為網頁服務的一部分。
• You need a more robust and potentially scalable solution for tool interaction.
  需要更穩健且具擴展性的工具互動方案。
• Your AI client (like some IDE extensions) primarily expects an HTTP endpoint for tools.
  你的 AI 客戶端（如某些 IDE 擴充功能）預期工具提供 HTTP 端點。

Both STDIO and SSE are powerful mechanisms within the Model Context Protocol, enabling your AI applications to become significantly more capable. By understanding their differences, you can make an informed decision and build more effective AI-powered solutions.
STDIO 和 SSE 都是模型上下文協定中的強大機制，能大幅提升 AI 應用能力。理解其差異有助做出明智決策，打造更高效的 AI 解決方案。

FAQ

1: What’s the simplest way to understand the core difference between STDIO and SSE in MCP?
Q1：如何最簡單理解 MCP 中 STDIO 與 SSE 的核心差異？
A:
* STDIO: Think local command-line scripts. Your AI tells a script on your computer to run, gives it input, and gets output back directly.
• STDIO：想像成本地命令列腳本。AI 指示你電腦上的腳本執行，給予輸入後直接獲取輸出。
* SSE: Think mini web service. Your AI talks to a tool over a network connection (even if it’s just on your local machine via localhost), like accessing a webpage.
• SSE：想像成微型網頁服務。AI 透過網路連線（即使只是本機的 localhost）與工具溝通，就像瀏覽網頁。

Q2: When should I choose STDIO, and when is SSE the better option?
Q2：何時該選 STDIO？何時 SSE 更合適？
A:
* Use STDIO if: Your tool is a local script, you need quick, simple integration for personal use, and it doesn’t need to be accessed over a network.
• 使用 STDIO 若：工具是本地腳本、需快速簡單整合自用、無需網路存取。
* Use SSE if: Your tool needs to be accessible over a network (even locally as http://localhost), you plan to share or publish it online, or your AI client primarily expects an HTTP endpoint.
• 使用 SSE 若：工具需網路存取（即使是本地 http://localhost）、計劃線上分享或發布、或 AI 客戶端預期 HTTP 端點。

Q3: So, STDIO is strictly for local machine use, and SSE is for network/web accessible tools?
Q3：所以 STDIO 僅限本機使用，SSE 用於網路/網頁工具？
A: Essentially, yes. STDIO operates via local process communication (like running a terminal command). SSE uses HTTP, which inherently allows for network communication, whether that’s localhost for local development or a public web address for wider access.
A：基本上正確。STDIO 透過本地行程通訊運作（如執行終端指令）；SSE 使用 HTTP 協定，天生支援網路通訊——無論是本地開發用的 localhost 或公開網址。

Q4: Are there significant performance or complexity differences?
Q4：兩者在效能或複雜度上有顯著差異嗎？
A:
* STDIO: Generally very low overhead and simpler to set up for existing command-line tools.
* STDIO：通常開銷極低，且對現有命令列工具的設定更為簡單。
* SSE: Involves network communication (adding slight latency) and requires setting up a basic web server, which adds a bit more complexity than a simple script. However, for most tool interactions, the tool’s own execution time is the dominant factor.
* SSE：涉及網路傳輸（會增加些微延遲），需架設基礎網頁伺服器，比單純腳本複雜些。不過多數工具互動中，工具本身的執行時間仍是主要影響因素。

Q5: What are the main security considerations for STDIO vs. SSE?
Q5：STDIO 與 SSE 的主要安全考量為何？
A:
* STDIO: Security is tied to your local user permissions. The AI agent runs scripts with the same privileges as the user running the agent.
* STDIO：安全性取決於本機使用者權限。AI 代理程式執行腳本時，會擁有與啟動代理程式者相同的操作權限。
* SSE: If exposed to a network (especially the public internet), requires standard web security: HTTPS, authentication, and authorization. For localhost SSE servers, the risk is lower but internal network access should still be considered.
* SSE：若暴露於網路（特別是公共網路），需具備標準網頁安全措施：HTTPS 加密、身份驗證與授權機制。本機端 SSE 伺服器風險較低，但仍須考量內部網路存取權限問題。