Prompts

A prompt is a message template the user picks.

Tools are for the model. A prompt is the opposite: the user chooses one from a menu in their client (a slash command, a button), fills in its arguments, and the rendered messages go into the conversation as if they had typed them.

You declare one by putting @mcp.prompt() on a function that returns the text.

Your first prompt

--8<-- "docs_src/prompts/tutorial001.py"

The SDK reads the same three things it reads from a tool:

• The name is the function name: review_code.
• The description the client shows is the docstring: Review a piece of code.
• The arguments come from the parameters. code has no default, so it's required.

That is what a client gets back from prompts/list:

{
  "name": "review_code",
  "description": "Review a piece of code.",
  "arguments": [
    {"name": "code", "required": true}
  ]
}

There is no JSON Schema here. Prompt arguments are a flat list of named string values: a form a person fills in, not a payload a model constructs.

Rendering it

The client renders the template with prompts/get, passing the arguments. Your function runs and the str you return becomes one user message:

{
  "description": "Review a piece of code.",
  "messages": [
    {
      "role": "user",
      "content": {
        "type": "text",
        "text": "Please review this code:\n\ndef add(a, b): return a + b"
      }
    }
  ],
  "resultType": "complete"
}

That is the entire life of a prompt: listed by name, rendered on demand, dropped into the chat.

!!! check required is enforced before your function runs. Render review_code without code and the request itself fails with a JSON-RPC error (code -32603):

```text
mcp.shared.exceptions.MCPError: Internal server error
```

There is no tool-style error result to hand back to a model, because no model is in the loop:
the call raises. The reason (`Missing required arguments: {'code'}`) lands in your server's log.

Try it

Run the server with the MCP Inspector:

uv run mcp dev server.py

Open the Prompts tab and select review_code. The Inspector draws a form with one required code field. Fill it in, render it, and you get back exactly the user message above.

More than one message

A code review is one message. A debugging session is a conversation, and a prompt can seed the whole thing.

Return a list of messages instead of a str:

--8<-- "docs_src/prompts/tutorial002.py"

• UserMessage and AssistantMessage come from mcp.server.mcpserver.prompts.base. Hand them a str and they wrap it in TextContent for you. The role is the class name.
• Message is their common base. Use it as the return annotation.

Rendering debug_error now produces three messages, in order:

{
  "description": "Start a debugging conversation.",
  "messages": [
    {"role": "user", "content": {"type": "text", "text": "I'm seeing this error:"}},
    {"role": "user", "content": {"type": "text", "text": "TypeError: 'int' object is not iterable"}},
    {
      "role": "assistant",
      "content": {"type": "text", "text": "I'll help debug that. What have you tried so far?"}
    }
  ],
  "resultType": "complete"
}

Notice the last one. Pre-filling an assistant turn is how you steer the model's next reply without making the user type the steering themselves.

Titles and argument descriptions

review_code is a function name, not a label. Give the client something better to put on the button, and describe each argument so the form explains itself:

--8<-- "docs_src/prompts/tutorial003.py"

• title="Code review" is the human-readable name, exactly like a tool's title.
• Annotated[str, Field(description=...)] is the same pattern Tools uses to describe a tool's parameters. Here the description lands on the argument instead of in a schema.
• language has a default, so it stops being required.

The prompts/list entry now carries everything a client needs to draw a good form:

{
  "name": "review_code",
  "title": "Code review",
  "description": "Review a piece of code.",
  "arguments": [
    {"name": "code", "description": "The code to review.", "required": true},
    {"name": "language", "description": "The language the code is written in.", "required": false}
  ]
}

!!! info If you have read Tools, you already know everything up to this point. Same decorator, same docstring-as-description, same Annotated/Field. The only things that change are who triggers it (the user) and where the result goes (into the conversation).

More than text

UserMessage and AssistantMessage also accept a content block, or an Image / Audio helper, wherever they accept a str. Two cases come up in prompts: attaching a document and attaching a picture.

Embedding a file

--8<-- "docs_src/prompts/tutorial004.py"

• The style guide is a resource at style://python (Resources covers those), read from a style-guide.md next to server.py. Put any Markdown file there.
• EmbeddedResource(resource=TextResourceContents(...)), both from mcp.types, carries the file with its URI and MIME type as the first message; the request that refers to it follows as plain text.
• Embedding, rather than pasting the guide into the f-string, lets the client show it as an attachment and reopen style://python later, and the model receives the file verbatim. For a binary file use BlobResourceContents with a base64 blob.

Rendered, the first message's content is a resource block:

{"type": "resource", "resource": {"uri": "style://python", "mimeType": "text/markdown", "text": "* Prefer early returns.\n..."}}

Attaching an image

--8<-- "docs_src/prompts/tutorial005.py"

• Image is the helper from Images, audio & icons. UserMessage converts it to an ImageContent block (the file base64-encoded, MIME type guessed from .png) when the prompt renders; Audio becomes an AudioContent the same way.
• Put any PNG named architecture.png beside server.py. Prompt arguments are strings, so the picture always comes from the server; component only supplies the words.

{"type": "image", "data": "iVBORw0KGgoAAAANSUhEUg...", "mimeType": "image/png"}

Changing the list at runtime

Prompts can be added while clients are connected, for example to let a user save an instruction as a menu entry of their own. Register the prompt, then notify:

--8<-- "docs_src/prompts/tutorial006.py"

• mcp.add_prompt(Prompt.from_function(fn, name=..., description=...)) registers a function exactly as @mcp.prompt() would, and mcp.remove_prompt(name) is the reverse. add_prompt keeps an existing entry of the same name rather than overwrite it, so the tool removes any old one first to make saving a replace. prompts/list reflects the change immediately.
• await ctx.notify_prompts_changed() sends notifications/prompts/list_changed to every 2026-07-28 client listening on a subscriptions/listen stream (Subscriptions). await ctx.session.send_prompt_list_changed() sends it to the calling client when that client is pre-2026 (Serving legacy clients). Call both; each does nothing when there is nobody to tell.
• A client that receives the notification calls prompts/list again. In the Python Client that is async with client.listen(prompts_list_changed=True) as sub:, which yields a PromptsListChanged event.

Recap

• @mcp.prompt() on a function makes it a prompt. Name from the function, description from the docstring.
• Prompts are user-controlled: the client lists them, the user picks one and fills in the arguments.
• Arguments are a flat list of named strings (no schema). A parameter with a default is optional.
• Return a str and it becomes one user message. Return a list of UserMessage / AssistantMessage to seed a multi-turn conversation.
• title= and Field(description=...) are what a client puts in its UI.
• A missing required argument fails the whole request. There is no per-prompt error result.
• Wrap an EmbeddedResource or an Image in a UserMessage to attach a document or a picture.
• Add or remove prompts at runtime with mcp.add_prompt(...) / mcp.remove_prompt(...), then await ctx.notify_prompts_changed() and await ctx.session.send_prompt_list_changed().

Server-side autocomplete for a prompt's (or a resource template's) arguments is Completions.