Merge pull request #729 from jlowin/parens

Remove empty parens from decorators in docs

README.md

@@ -31,7 +31,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP("Demo 🚀")
 
-@mcp.tool()
+@mcp.tool
 def add(a: int, b: int) -> int:
     """Add two numbers"""
     return a + b
@@ -144,7 +144,7 @@ Learn more in the [**FastMCP Server Documentation**](https://gofastmcp.com/serve
 Tools allow LLMs to perform actions by executing your Python functions (sync or async). Ideal for computations, API calls, or side effects (like `POST`/`PUT`). FastMCP handles schema generation from type hints and docstrings. Tools can return various types, including text, JSON-serializable objects, and even images using the [`fastmcp.Image`](https://gofastmcp.com/servers/tools#return-values) helper.
 
 ```python
-@mcp.tool()
+@mcp.tool
 def multiply(a: float, b: float) -> float:
     """Multiplies two numbers."""
     return a * b
@@ -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}"
@@ -201,7 +201,7 @@ from fastmcp import FastMCP, Context
 
 mcp = FastMCP("My MCP Server")
 
-@mcp.tool()
+@mcp.tool
 async def process_data(uri: str, ctx: Context):
     # Log a message to the client
     await ctx.info(f"Processing {uri}...")
@@ -321,7 +321,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP("Demo 🚀")
 
-@mcp.tool()
+@mcp.tool
 def hello(name: str) -> str:
     return f"Hello, {name}!"
 

docs/clients/transports.mdx

@@ -359,7 +359,7 @@ import asyncio
 # 1. Create your FastMCP server instance
 server = FastMCP(name="InMemoryServer")
 
-@server.tool()
+@server.tool
 def ping(): 
     return "pong"
 

docs/deployment/asgi.mdx

@@ -32,7 +32,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP("MyServer")
 
-@mcp.tool()
+@mcp.tool
 def hello(name: str) -> str:
     return f"Hello, {name}!"
 

docs/deployment/running-server.mdx

@@ -22,7 +22,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="MyServer")
 
-@mcp.tool()
+@mcp.tool
 def hello(name: str) -> str:
     return f"Hello, {name}!"
 
@@ -244,7 +244,7 @@ import asyncio
 
 mcp = FastMCP(name="MyServer")
 
-@mcp.tool()
+@mcp.tool
 def hello(name: str) -> str:
     return f"Hello, {name}!"
 

docs/getting-started/quickstart.mdx

@@ -32,7 +32,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP("My MCP Server")
 
-@mcp.tool()
+@mcp.tool
 def greet(name: str) -> str:
     return f"Hello, {name}!"
 ```
@@ -49,7 +49,7 @@ from fastmcp import FastMCP, Client
 
 mcp = FastMCP("My MCP Server")
 
-@mcp.tool()
+@mcp.tool
 def greet(name: str) -> str:
     return f"Hello, {name}!"
 
@@ -76,7 +76,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP("My MCP Server")
 
-@mcp.tool()
+@mcp.tool
 def greet(name: str) -> str:
     return f"Hello, {name}!"
 

docs/getting-started/welcome.mdx

@@ -14,7 +14,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP("Demo 🚀")
 
-@mcp.tool()
+@mcp.tool
 def add(a: int, b: int) -> int:
     """Add two numbers"""
     return a + b

docs/integrations/anthropic.mdx

@@ -28,7 +28,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="Dice Roller")
 
-@mcp.tool()
+@mcp.tool
 def roll_dice(n_dice: int) -> list[int]:
     """Roll `n_dice` 6-sided dice and return the results."""
     return [random.randint(1, 6) for _ in range(n_dice)]
@@ -171,7 +171,7 @@ auth = BearerAuthProvider(
 
 mcp = FastMCP(name="Dice Roller", auth=auth)
 
-@mcp.tool()
+@mcp.tool
 def roll_dice(n_dice: int) -> list[int]:
     """Roll `n_dice` 6-sided dice and return the results."""
     return [random.randint(1, 6) for _ in range(n_dice)]

docs/integrations/claude-desktop.mdx

@@ -31,7 +31,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="Dice Roller")
 
-@mcp.tool()
+@mcp.tool
 def roll_dice(n_dice: int) -> list[int]:
     """Roll `n_dice` 6-sided dice and return the results."""
     return [random.randint(1, 6) for _ in range(n_dice)]

docs/integrations/gemini.mdx

@@ -32,7 +32,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="Dice Roller")
 
-@mcp.tool()
+@mcp.tool
 def roll_dice(n_dice: int) -> list[int]:
     """Roll `n_dice` 6-sided dice and return the results."""
     return [random.randint(1, 6) for _ in range(n_dice)]

docs/integrations/openai.mdx

@@ -33,7 +33,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="Dice Roller")
 
-@mcp.tool()
+@mcp.tool
 def roll_dice(n_dice: int) -> list[int]:
     """Roll `n_dice` 6-sided dice and return the results."""
     return [random.randint(1, 6) for _ in range(n_dice)]
@@ -166,7 +166,7 @@ auth = BearerAuthProvider(
 
 mcp = FastMCP(name="Dice Roller", auth=auth)
 
-@mcp.tool()
+@mcp.tool
 def roll_dice(n_dice: int) -> list[int]:
     """Roll `n_dice` 6-sided dice and return the results."""
     return [random.randint(1, 6) for _ in range(n_dice)]

docs/patterns/cli.mdx

@@ -66,7 +66,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP("MyServer")
 
-@mcp.tool()
+@mcp.tool
 def hello(name: str) -> str:
     return f"Hello, {name}!"
 

docs/patterns/decorating-methods.mdx

@@ -5,11 +5,11 @@ 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?
 
-When you apply a FastMCP decorator like `@tool`, `@resource()`, or `@prompt()` to a method, the decorator captures the function at decoration time. For instance methods and class methods, this poses a challenge because:
+When you apply a FastMCP decorator like `@tool`, `@resource`, or `@prompt` to a method, the decorator captures the function at decoration time. For instance methods and class methods, this poses a challenge because:
 
 1. For instance methods: The decorator gets the unbound method before any instance exists
 2. For class methods: The decorator gets the function before it's bound to the class
@@ -29,7 +29,7 @@ from fastmcp import FastMCP
 mcp = FastMCP()
 
 class MyClass:
-    @mcp.tool()
+    @mcp.tool
     def my_method(self, x: int) -> int:
         return x * 2
 
@@ -53,7 +53,7 @@ from fastmcp import FastMCP
 mcp = FastMCP()
 
 class MyClass:
-    @mcp.tool()  # This won't work correctly
+    @mcp.tool  # This won't work correctly
     def add(self, x, y):
         return x + y
 ```
@@ -100,19 +100,19 @@ mcp = FastMCP()
 
 class MyClass:
     @classmethod
-    @mcp.tool()  # This won't work but won't raise an error
+    @mcp.tool  # This won't work but won't raise an error
     def from_string_v1(cls, s):
         return cls(s)
     
-    @mcp.tool()
+    @mcp.tool
     @classmethod  # This will raise a helpful ValueError
     def from_string_v2(cls, s):
         return cls(s)
 ```
 </Warning>
 
-- If `@classmethod` comes first, then `@mcp.tool()`: No error is raised, but it won't work correctly
-- If `@mcp.tool()` comes first, then `@classmethod`: FastMCP will detect this and raise a helpful `ValueError` with guidance
+- If `@classmethod` comes first, then `@mcp.tool`: No error is raised, but it won't work correctly
+- If `@mcp.tool` comes first, then `@classmethod`: FastMCP will detect this and raise a helpful `ValueError` with guidance
 
 <Check>
 **Do this instead**:
@@ -150,7 +150,7 @@ from fastmcp import FastMCP
 mcp = FastMCP()
 
 class MyClass:
-    @mcp.tool()
+    @mcp.tool
     @staticmethod
     def utility(x, y):
         return x + y

docs/patterns/http-requests.mdx

@@ -25,7 +25,7 @@ from starlette.requests import Request
 
 mcp = FastMCP(name="HTTP Request Demo")
 
-@mcp.tool()
+@mcp.tool
 async def user_agent_info() -> dict:
     """Return information about the user agent."""
     # Get the HTTP request
@@ -58,7 +58,7 @@ from fastmcp.server.dependencies import get_http_headers
 
 mcp = FastMCP(name="Headers Demo")
 
-@mcp.tool()
+@mcp.tool
 async def safe_header_info() -> dict:
     """Safely get header information without raising errors."""
     # Get headers (returns empty dict if no request context)

docs/patterns/testing.mdx

@@ -22,7 +22,7 @@ from fastmcp import FastMCP, Client
 def mcp_server():
     server = FastMCP("TestServer")
     
-@server.tool()
+@server.tool
     def greet(name: str) -> str:
         return f"Hello, {name}!"
         

docs/servers/auth/bearer.mdx

@@ -160,7 +160,7 @@ Once authenticated, your tools, resources, or prompts can access token informati
 from fastmcp import FastMCP, Context, ToolError
 from fastmcp.server.dependencies import get_access_token, AccessToken
 
-@mcp.tool()
+@mcp.tool
 async def get_my_data(ctx: Context) -> dict:
     access_token: AccessToken = get_access_token()
     

docs/servers/composition.mdx

@@ -50,7 +50,7 @@ import asyncio
 # Define subservers
 weather_mcp = FastMCP(name="WeatherService")
 
-@weather_mcp.tool()
+@weather_mcp.tool
 def get_forecast(city: str) -> dict:
     """Get weather forecast."""
     return {"city": city, "forecast": "Sunny"}
@@ -102,7 +102,7 @@ from fastmcp import FastMCP, Client
 # Define subserver
 dynamic_mcp = FastMCP(name="DynamicService")
 
-@dynamic_mcp.tool()
+@dynamic_mcp.tool
 def initial_tool():
     """Initial tool demonstration."""
     return "Initial Tool Exists"
@@ -112,7 +112,7 @@ main_mcp = FastMCP(name="MainAppLive")
 main_mcp.mount("dynamic", dynamic_mcp)
 
 # Add a tool AFTER mounting - it will be accessible through main_mcp
-@dynamic_mcp.tool()
+@dynamic_mcp.tool
 def added_later():
     """Tool added after mounting."""
     return "Tool Added Dynamically!"

docs/servers/context.mdx

@@ -41,7 +41,7 @@ from fastmcp import FastMCP, Context
 
 mcp = FastMCP(name="ContextDemo")
 
-@mcp.tool()
+@mcp.tool
 async def process_file(file_uri: str, ctx: Context) -> str:
     """Processes a file, using context for logging and resource access."""
     # Context is available as the ctx parameter
@@ -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
@@ -99,7 +99,7 @@ async def process_data(data: list[float]) -> dict:
     ctx = get_context()    
     await ctx.info(f"Processing {len(data)} data points")
     
-@mcp.tool()
+@mcp.tool
 async def analyze_dataset(dataset_name: str) -> dict:
     # Call utility function that uses context internally
     data = load_data(dataset_name)
@@ -118,7 +118,7 @@ async def analyze_dataset(dataset_name: str) -> dict:
 Send log messages back to the MCP client. This is useful for debugging and providing visibility into function execution during a request.
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def analyze_data(data: list[float], ctx: Context) -> dict:
     """Analyze numerical data with logging."""
     await ctx.debug("Starting analysis of numerical data")
@@ -149,7 +149,7 @@ async def analyze_data(data: list[float], ctx: Context) -> dict:
 For long-running operations, notify the client about the progress. This allows clients to display progress indicators and provide a better user experience.
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def process_items(items: list[str], ctx: Context) -> dict:
     """Process a list of items with progress updates."""
     total = len(items)
@@ -182,7 +182,7 @@ Progress reporting requires the client to have sent a `progressToken` in the ini
 Read data from resources registered with your FastMCP server. This allows functions to access files, configuration, or dynamically generated content.
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def summarize_document(document_uri: str, ctx: Context) -> str:
     """Summarize a document by its resource URI."""
     # Read the document content
@@ -222,7 +222,7 @@ The returned content is typically accessed via `content_list[0].content` and can
 Request the client's LLM to generate text based on provided messages. This is useful when your function needs to leverage the LLM's capabilities to process data or generate responses.
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def analyze_sentiment(text: str, ctx: Context) -> dict:
     """Analyze the sentiment of a text using the client's LLM."""
     # Create a sampling prompt asking for sentiment analysis
@@ -258,7 +258,7 @@ async def analyze_sentiment(text: str, ctx: Context) -> dict:
 When providing a simple string, it's treated as a user message. For more complex scenarios, you can provide a list of messages with different roles.
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def generate_example(concept: str, ctx: Context) -> str:
     """Generate a Python code example for a given concept."""
     # Using a system prompt and a user message
@@ -280,7 +280,7 @@ See [Client Sampling](/clients/client#llm-sampling) for more details on how clie
 Access metadata about the current request and client.
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def request_info(ctx: Context) -> dict:
     """Return information about the current request."""
     return {
@@ -300,7 +300,7 @@ async def request_info(ctx: Context) -> dict:
 #### FastMCP Server and Sessions
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def advanced_tool(ctx: Context) -> str:
     """Demonstrate advanced context access."""
     # Access the FastMCP server instance
@@ -326,7 +326,7 @@ See the [HTTP Requests pattern](/patterns/http-requests) for more details.
 For web applications, you can access the underlying HTTP request:
 
 ```python
-@mcp.tool()
+@mcp.tool
 async def handle_web_request(ctx: Context) -> dict:
     """Access HTTP request information from the Starlette request."""
     request = ctx.get_http_request()

docs/servers/fastmcp.mdx

@@ -47,7 +47,7 @@ FastMCP servers expose several types of components to the client:
 Tools are functions that the client can call to perform actions or access external systems.
 
 ```python
-@mcp.tool()
+@mcp.tool
 def multiply(a: float, b: float) -> float:
     """Multiplies two numbers together."""
     return a * b
@@ -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)
@@ -106,7 +106,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="MyServer")
 
-@mcp.tool()
+@mcp.tool
 def greet(name: str) -> str:
     """Greet a user by name."""
     return f"Hello, {name}!"
@@ -145,7 +145,7 @@ import asyncio
 main = FastMCP(name="Main")
 sub = FastMCP(name="Sub")
 
-@sub.tool()
+@sub.tool
 def hello(): 
     return "hi"
 
@@ -216,7 +216,7 @@ def yaml_serializer(data):
 # Create a server with the custom serializer
 mcp = FastMCP(name="MyServer", tool_serializer=yaml_serializer)
 
-@mcp.tool()
+@mcp.tool
 def get_config():
     """Returns configuration in YAML format."""
     return {"api_key": "abc123", "debug": True, "rate_limit": 100}

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

docs/servers/proxy.mdx

@@ -90,7 +90,7 @@ from fastmcp import FastMCP
 # Original server
 original_server = FastMCP(name="Original")
 
-@original_server.tool()
+@original_server.tool
 def tool_a() -> str: 
     return "A"
 

docs/servers/tools.mdx

@@ -31,7 +31,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="CalculatorServer")
 
-@mcp.tool()
+@mcp.tool
 def add(a: int, b: int) -> int:
     """Adds two integer numbers together."""
     return a + b
@@ -61,7 +61,7 @@ Type annotations for parameters are essential for proper tool functionality. The
 Use standard Python type annotations for parameters:
 
 ```python
-@mcp.tool()
+@mcp.tool
 def analyze_text(
     text: str,
     max_tokens: int = 100,
@@ -79,7 +79,7 @@ You can provide additional metadata about parameters using Pydantic's `Field` cl
 from typing import Annotated
 from pydantic import Field
 
-@mcp.tool()
+@mcp.tool
 def process_image(
     image_url: Annotated[str, Field(description="URL of the image to process")],
     resize: Annotated[bool, Field(description="Whether to resize the image")] = False,
@@ -97,7 +97,7 @@ def process_image(
 You can also use the Field as a default value, though the Annotated approach is preferred:
 
 ```python
-@mcp.tool()
+@mcp.tool
 def search_database(
     query: str = Field(description="Search query string"),
     limit: int = Field(10, description="Maximum number of results", ge=1, le=100)
@@ -137,7 +137,7 @@ For additional type annotations not listed here, see the [Parameter Types](#para
 FastMCP follows Python's standard function parameter conventions. Parameters without default values are required, while those with default values are optional.
 
 ```python
-@mcp.tool()
+@mcp.tool
 def search_products(
     query: str,                   # Required - no default value
     max_results: int = 10,        # Optional - has default value
@@ -197,14 +197,14 @@ FastMCP seamlessly supports both standard (`def`) and asynchronous (`async def`)
 
 ```python
 # Synchronous tool (suitable for CPU-bound or quick tasks)
-@mcp.tool()
+@mcp.tool
 def calculate_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
     """Calculate the distance between two coordinates."""
     # Implementation...
     return 42.5
 
 # Asynchronous tool (ideal for I/O-bound operations)
-@mcp.tool()
+@mcp.tool
 async def fetch_weather(city: str) -> dict:
     """Retrieve current weather conditions for a city."""
     # Use 'async def' for operations involving network calls, file I/O, etc.
@@ -244,7 +244,7 @@ except ImportError:
 
 mcp = FastMCP("Image Demo")
 
-@mcp.tool()
+@mcp.tool
 def generate_image(width: int, height: int, color: str) -> Image:
     """Generates a solid color image."""
     # Create image using Pillow
@@ -258,7 +258,7 @@ def generate_image(width: int, height: int, color: str) -> Image:
     # Return using FastMCP's Image helper
     return Image(data=img_bytes, format="png")
 
-@mcp.tool()
+@mcp.tool
 def do_nothing() -> None:
     """This tool performs an action but returns no data."""
     print("Performing a side effect...")
@@ -285,7 +285,7 @@ mcp = FastMCP(name="SecureServer", mask_error_details=True)
 from fastmcp import FastMCP
 from fastmcp.exceptions import ToolError
 
-@mcp.tool()
+@mcp.tool
 def divide(a: float, b: float) -> float:
     """Divide a by b."""
 
@@ -351,7 +351,7 @@ from fastmcp import FastMCP, Context
 
 mcp = FastMCP(name="ContextDemo")
 
-@mcp.tool()
+@mcp.tool
 async def process_data(data_uri: str, ctx: Context) -> dict:
     """Process data from a resource with progress reporting."""
     await ctx.info(f"Processing data from {data_uri}")
@@ -396,7 +396,7 @@ FastMCP supports **type coercion** when possible. This means that if a client se
 The most common parameter types are Python's built-in scalar types:
 
 ```python
-@mcp.tool()
+@mcp.tool
 def process_values(
     name: str,             # Text data
     count: int,            # Integer numbers
@@ -416,7 +416,7 @@ FastMCP supports various date and time types from the `datetime` module:
 ```python
 from datetime import datetime, date, timedelta
 
-@mcp.tool()
+@mcp.tool
 def process_date_time(
     event_date: date,             # ISO format date string or date object
     event_time: datetime,         # ISO format datetime string or datetime object
@@ -440,7 +440,7 @@ def process_date_time(
 FastMCP supports all standard Python collection types:
 
 ```python
-@mcp.tool()
+@mcp.tool
 def analyze_data(
     values: list[float],           # List of numbers
     properties: dict[str, str],    # Dictionary with string keys and values
@@ -465,7 +465,7 @@ Collection types can be nested and combined to represent complex data structures
 For parameters that can accept multiple types or may be omitted:
 
 ```python
-@mcp.tool()
+@mcp.tool
 def flexible_search(
     query: str | int,              # Can be either string or integer
     filters: dict[str, str] | None = None,  # Optional dictionary
@@ -488,7 +488,7 @@ Literals constrain parameters to a specific set of values:
 ```python
 from typing import Literal
 
-@mcp.tool()
+@mcp.tool
 def sort_data(
     data: list[float],
     order: Literal["ascending", "descending"] = "ascending",
@@ -516,7 +516,7 @@ class Color(Enum):
     GREEN = "green"
     BLUE = "blue"
 
-@mcp.tool()
+@mcp.tool
 def process_image(
     image_path: str, 
     color_filter: Color = Color.RED
@@ -539,7 +539,7 @@ There are two approaches to handling binary data in tool parameters:
 #### Bytes
 
 ```python
-@mcp.tool()
+@mcp.tool
 def process_binary(data: bytes):
     """Process binary data directly.
     
@@ -563,7 +563,7 @@ FastMCP does not automatically decode base64-encoded strings for bytes parameter
 from typing import Annotated
 from pydantic import Field
 
-@mcp.tool()
+@mcp.tool
 def process_image_data(
     image_data: Annotated[str, Field(description="Base64-encoded image data")]
 ):
@@ -587,7 +587,7 @@ The `Path` type from the `pathlib` module can be used for file system paths:
 ```python
 from pathlib import Path
 
-@mcp.tool()
+@mcp.tool
 def process_file(path: Path) -> str:
     """Process a file at the given path."""
     assert isinstance(path, Path)  # Path is properly converted
@@ -603,7 +603,7 @@ The `UUID` type from the `uuid` module can be used for unique identifiers:
 ```python
 import uuid
 
-@mcp.tool()
+@mcp.tool
 def process_item(
     item_id: uuid.UUID  # String UUID or UUID object
 ) -> str:
@@ -628,7 +628,7 @@ class User(BaseModel):
     age: int | None = None
     is_active: bool = True
 
-@mcp.tool()
+@mcp.tool
 def create_user(user: User):
     """Create a new user in the system."""
     # The input is automatically validated against the User model
@@ -657,7 +657,7 @@ Note that fields can be used *outside* Pydantic models to provide metadata and v
 from typing import Annotated
 from pydantic import Field
 
-@mcp.tool()
+@mcp.tool
 def analyze_metrics(
     # Numbers with range constraints
     count: Annotated[int, Field(ge=0, le=100)],         # 0 <= count <= 100
@@ -682,7 +682,7 @@ def analyze_metrics(
 You can also use `Field` as a default value, though the `Annotated` approach is preferred:
 
 ```python
-@mcp.tool()
+@mcp.tool
 def validate_data(
     # Value constraints
     age: int = Field(ge=0, lt=120),                     # 0 <= age < 120
@@ -727,7 +727,7 @@ mcp = FastMCP(
     on_duplicate_tools="error"
 )
 
-@mcp.tool()
+@mcp.tool
 def my_tool(): return "Version 1"
 
 # This will now raise a ValueError because 'my_tool' already exists
@@ -754,7 +754,7 @@ from fastmcp import FastMCP
 
 mcp = FastMCP(name="DynamicToolServer")
 
-@mcp.tool()
+@mcp.tool
 def calculate_sum(a: int, b: int) -> int:
     """Add two numbers together."""
     return a + b

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!"