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向主机请求补全

调用客户端来运行 LLM 提示词。

向主机请求补全 是 CoddyKit 上的免费 MCP Academy 课时。这是第 2 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MCP Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MCP Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

The Request Method

To ask for a generation, your server sends a sampling/createMessage request to the client over the protocol.

{ "method": "sampling/createMessage" }

Reach the Client via Context

Inside a tool you get a Context object. Its session is your line back to the client that can run sampling.

async def tool(topic: str, ctx: Context) -> str:
    ...

Call create_message

The Python SDK exposes ctx.session.create_message. Await it, pass your messages, and you get the model’s reply back.

result = await ctx.session.create_message(
    messages=[...],
    max_tokens=100,
)

Build a SamplingMessage

Each turn you send is a SamplingMessage with a role like user and a content block holding the actual text.

SamplingMessage(role="user",
    content=TextContent(type="text", text=prompt))

Wrap Text in TextContent

Plain text goes inside TextContent with type set to text. That typed wrapper is how MCP carries the message body.

TextContent(type="text", text="Summarize this report")

Always Set max_tokens

Give the model a ceiling with max_tokens. It caps how long the generated reply can be and keeps cost predictable.

max_tokens=200

Read the Result

The reply’s content is a typed block. Check its type, and if it is text, read result.content.text for the answer.

if result.content.type == "text":
    return result.content.text

Inspect Which Model Ran

The result also reports the model the client chose, so you can log exactly which model produced your output.

print(result.model)  # e.g. claude-3-sonnet-20240307

Know Why It Stopped

A stopReason field tells you why generation ended — like endTurn or hitting your max token limit.

result.stopReason  # "endTurn"

A Whole Tool, Briefly

So a sampling tool is: build a message, await create_message, then return the text — just a few lines.

r = await ctx.session.create_message(
    messages=[msg], max_tokens=100)
return r.content.text

It Can Be Rejected

Remember the request may be denied by the user. Treat a rejection like any failure and handle it gracefully.

Quick Check

One detail about making the call from your tool.

Recap

Send sampling/createMessage via ctx.session, wrap prompts in SamplingMessage and TextContent, set max_tokens, then read result.content.text. 💬

常见问题解答

「向主机请求补全」课时是免费的吗？

是的 — 「向主机请求补全」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MCP Academy 课程的其余内容，请升级到 CoddyKit PRO。 MCP Academy 课程共包含 4 节课。

「向主机请求补全」这节课中我会学到什么？

调用客户端来运行 LLM 提示词。你通过在浏览器中直接运行的动手代码来练习 MCP Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MCP Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MCP Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 2 节课，共 4 节。

「向主机请求补全」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MCP Academy 课中编写并运行代码吗？

能。每节 MCP Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

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