remove empty parens from prompt

README.md

@@ -176,7 +176,7 @@ Learn more in the [**Resources & Templates Documentation**](https://gofastmcp.co
 Prompts define reusable message templates to guide LLM interactions. Decorate functions with `@mcp.prompt`. Return strings or `Message` objects.
 
 ```python
-@mcp.prompt()
+@mcp.prompt
 def summarize_request(text: str) -> str:
     """Generate a prompt asking for a summary."""
     return f"Please summarize the following text:\n\n{text}"

docs/patterns/decorating-methods.mdx

@@ -5,7 +5,7 @@ description: Properly use instance methods, class methods, and static methods wi
 icon: at
 ---
 
-FastMCP's decorator system is designed to work with functions, but you may see unexpected behavior if you try to decorate an instance or class method. This guide explains the correct approach for using methods with all FastMCP decorators (`@tool`, `@resource()`, and `@prompt()`).
+FastMCP's decorator system is designed to work with functions, but you may see unexpected behavior if you try to decorate an instance or class method. This guide explains the correct approach for using methods with all FastMCP decorators (`@tool`, `@resource`, and `.prompt`).
 
 ## Why Are Methods Hard?
 

docs/servers/context.mdx

@@ -71,7 +71,7 @@ async def get_user_profile(user_id: str, ctx: Context) -> dict:
 <VersionBadge version="2.2.5" />
 
 ```python
-@mcp.prompt()
+@mcp.prompt
 async def data_analysis_request(dataset: str, ctx: Context) -> str:
     """Generate a request to analyze data with contextual information."""
     # Context is available as the ctx parameter

docs/servers/fastmcp.mdx

@@ -87,7 +87,7 @@ See [Resources & Templates](/servers/resources) for detailed documentation.
 Prompts are reusable message templates for guiding the LLM.
 
 ```python
-@mcp.prompt()
+@mcp.prompt
 def analyze_data(data_points: list[float]) -> str:
     """Creates a prompt asking for analysis of numerical data."""
     formatted_data = ", ".join(str(point) for point in data_points)

docs/servers/prompts.mdx

@@ -33,13 +33,13 @@ from fastmcp.prompts.prompt import Message, PromptMessage, TextContent
 mcp = FastMCP(name="PromptServer")
 
 # Basic prompt returning a string (converted to user message automatically)
-@mcp.prompt()
+@mcp.prompt
 def ask_about_topic(topic: str) -> str:
     """Generates a user message asking for an explanation of a topic."""
     return f"Can you please explain the concept of '{topic}'?"
 
 # Prompt returning a specific message type
-@mcp.prompt()
+@mcp.prompt
 def generate_code_request(language: str, task_description: str) -> PromptMessage:
     """Generates a user message requesting code generation."""
     content = f"Write a {language} function that performs the following task: {task_description}"
@@ -69,7 +69,7 @@ FastMCP intelligently handles different return types from your prompt function:
 ```python
 from fastmcp.prompts.prompt import Message
 
-@mcp.prompt()
+@mcp.prompt
 def roleplay_scenario(character: str, situation: str) -> list[Message]:
     """Sets up a roleplaying scenario with initial messages."""
     return [
@@ -89,7 +89,7 @@ Type annotations are important for prompts. They:
 from pydantic import Field
 from typing import Literal, Optional
 
-@mcp.prompt()
+@mcp.prompt
 def generate_content_request(
     topic: str = Field(description="The main subject to cover"),
     format: Literal["blog", "email", "social"] = "blog",
@@ -111,7 +111,7 @@ def generate_content_request(
 Parameters in your function signature are considered **required** unless they have a default value.
 
 ```python
-@mcp.prompt()
+@mcp.prompt
 def data_analysis_prompt(
     data_uri: str,                        # Required - no default value
     analysis_type: str = "summary",       # Optional - has default value
@@ -154,13 +154,13 @@ FastMCP seamlessly supports both standard (`def`) and asynchronous (`async def`)
 
 ```python
 # Synchronous prompt
-@mcp.prompt()
+@mcp.prompt
 def simple_question(question: str) -> str:
     """Generates a simple question to ask the LLM."""
     return f"Question: {question}"
 
 # Asynchronous prompt
-@mcp.prompt()
+@mcp.prompt
 async def data_based_prompt(data_id: str) -> str:
     """Generates a prompt based on data that needs to be fetched."""
     # In a real scenario, you might fetch data from a database or API
@@ -183,7 +183,7 @@ from fastmcp import FastMCP, Context
 
 mcp = FastMCP(name="PromptServer")
 
-@mcp.prompt()
+@mcp.prompt
 async def generate_report_request(report_type: str, ctx: Context) -> str:
     """Generates a request for a report."""
     return f"Please create a {report_type} report. Request ID: {ctx.request_id}"
@@ -207,7 +207,7 @@ mcp = FastMCP(
     on_duplicate_prompts="error"  # Raise an error if a prompt name is duplicated
 )
 
-@mcp.prompt()
+@mcp.prompt
 def greeting(): return "Hello, how can I help you today?"
 
 # This registration attempt will raise a ValueError because

tests/server/test_server_interactions.py

@@ -1086,7 +1086,7 @@ class TestPrompts:
     async def test_prompt_decorator_with_parens(self):
         mcp = FastMCP()
 
-        @mcp.prompt()
+        @mcp.prompt
         def fn() -> str:
             return "Hello, world!"