# 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 ```python title="server.py" hl_lines="6-9" --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`: ```json { "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**: ```json { "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: ```console 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`: ```python title="server.py" hl_lines="2 13-20" --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: ```json { "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: ```python title="server.py" hl_lines="10-13" --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](tools.md)** 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: ```json { "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](tools.md)**, you already know everything on this page. 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). ## 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. Server-side autocomplete for a prompt's (or a resource template's) arguments is **[Completions](completions.md)**.