feat: Add structured output support for tool functions

README.md

@@ -27,6 +27,7 @@
     - [Server](#server)
     - [Resources](#resources)
     - [Tools](#tools)
+      - [Structured Output](#structured-output)
     - [Prompts](#prompts)
     - [Images](#images)
     - [Context](#context)
@@ -249,6 +250,127 @@ async def fetch_weather(city: str) -> str:
         return response.text
 ```
 
+#### Structured Output
+
+Tools will return structured results by default, if their return type
+annotation is compatible. Otherwise, they will return unstructured results. 
+
+Structured output supports these return types:
+- Pydantic models (BaseModel subclasses)
+- TypedDicts
+- Dataclasses and other classes with type hints
+- `dict[str, T]` (where T is any JSON-serializable type)
+- Primitive types (str, int, float, bool, bytes, None) - wrapped in `{"result": value}`
+- Generic types (list, tuple, Union, Optional, etc.) - wrapped in `{"result": value}`
+
+Classes without type hints cannot be serialized for structured output. Only
+classes with properly annotated attributes will be converted to Pydantic models
+for schema generation and validation.
+
+Structured results are automatically validated against the output schema 
+generated from the annotation. This ensures the tool returns well-typed, 
+validated data that clients can easily process.
+
+**Note:** For backward compatibility, unstructured results are also
+returned. Unstructured results are provided for backward compatibility 
+with previous versions of the MCP specification, and are quirks-compatible
+with previous versions of FastMCP in the current version of the SDK.
+
+**Note:** In cases where a tool function's return type annotation 
+causes the tool to be classified as structured _and this is undesirable_, 
+the  classification can be suppressed by passing `structured_output=False`
+to the `@tool` decorator.
+
+```python
+from mcp.server.fastmcp import FastMCP
+from pydantic import BaseModel, Field
+from typing import TypedDict
+
+mcp = FastMCP("Weather Service")
+
+
+# Using Pydantic models for rich structured data
+class WeatherData(BaseModel):
+    temperature: float = Field(description="Temperature in Celsius")
+    humidity: float = Field(description="Humidity percentage")
+    condition: str
+    wind_speed: float
+
+
+@mcp.tool()
+def get_weather(city: str) -> WeatherData:
+    """Get structured weather data"""
+    return WeatherData(
+        temperature=22.5, humidity=65.0, condition="partly cloudy", wind_speed=12.3
+    )
+
+
+# Using TypedDict for simpler structures
+class LocationInfo(TypedDict):
+    latitude: float
+    longitude: float
+    name: str
+
+
+@mcp.tool()
+def get_location(address: str) -> LocationInfo:
+    """Get location coordinates"""
+    return LocationInfo(latitude=51.5074, longitude=-0.1278, name="London, UK")
+
+
+# Using dict[str, Any] for flexible schemas
+@mcp.tool()
+def get_statistics(data_type: str) -> dict[str, float]:
+    """Get various statistics"""
+    return {"mean": 42.5, "median": 40.0, "std_dev": 5.2}
+
+
+# Ordinary classes with type hints work for structured output
+class UserProfile:
+    name: str
+    age: int
+    email: str | None = None
+
+    def __init__(self, name: str, age: int, email: str | None = None):
+        self.name = name
+        self.age = age
+        self.email = email
+
+
+@mcp.tool()
+def get_user(user_id: str) -> UserProfile:
+    """Get user profile - returns structured data"""
+    return UserProfile(name="Alice", age=30, email="alice@example.com")
+
+
+# Classes WITHOUT type hints cannot be used for structured output
+class UntypedConfig:
+    def __init__(self, setting1, setting2):
+        self.setting1 = setting1
+        self.setting2 = setting2
+
+
+@mcp.tool()
+def get_config() -> UntypedConfig:
+    """This returns unstructured output - no schema generated"""
+    return UntypedConfig("value1", "value2")
+
+
+# Lists and other types are wrapped automatically
+@mcp.tool()
+def list_cities() -> list[str]:
+    """Get a list of cities"""
+    return ["London", "Paris", "Tokyo"]
+    # Returns: {"result": ["London", "Paris", "Tokyo"]}
+
+
+@mcp.tool()
+def get_temperature(city: str) -> float:
+    """Get temperature as a simple float"""
+    return 22.5
+    # Returns: {"result": 22.5}
+```
+
 ### Prompts
 
 Prompts are reusable templates that help LLMs interact with your server effectively:

examples/fastmcp/weather_structured.py

@@ -0,0 +1,225 @@
+"""
+FastMCP Weather Example with Structured Output
+
+Demonstrates how to use structured output with tools to return
+well-typed, validated data that clients can easily process.
+"""
+
+import asyncio
+import json
+import sys
+from dataclasses import dataclass
+from datetime import datetime
+from typing import TypedDict
+
+from pydantic import BaseModel, Field
+
+from mcp.server.fastmcp import FastMCP
+from mcp.shared.memory import create_connected_server_and_client_session as client_session
+
+# Create server
+mcp = FastMCP("Weather Service")
+
+
+# Example 1: Using a Pydantic model for structured output
+class WeatherData(BaseModel):
+    """Structured weather data response"""
+
+    temperature: float = Field(description="Temperature in Celsius")
+    humidity: float = Field(description="Humidity percentage (0-100)")
+    condition: str = Field(description="Weather condition (sunny, cloudy, rainy, etc.)")
+    wind_speed: float = Field(description="Wind speed in km/h")
+    location: str = Field(description="Location name")
+    timestamp: datetime = Field(default_factory=datetime.now, description="Observation time")
+
+
+@mcp.tool()
+def get_weather(city: str) -> WeatherData:
+    """Get current weather for a city with full structured data"""
+    # In a real implementation, this would fetch from a weather API
+    return WeatherData(temperature=22.5, humidity=65.0, condition="partly cloudy", wind_speed=12.3, location=city)
+
+
+# Example 2: Using TypedDict for a simpler structure
+class WeatherSummary(TypedDict):
+    """Simple weather summary"""
+
+    city: str
+    temp_c: float
+    description: str
+
+
+@mcp.tool()
+def get_weather_summary(city: str) -> WeatherSummary:
+    """Get a brief weather summary for a city"""
+    return WeatherSummary(city=city, temp_c=22.5, description="Partly cloudy with light breeze")
+
+
+# Example 3: Using dict[str, Any] for flexible schemas
+@mcp.tool()
+def get_weather_metrics(cities: list[str]) -> dict[str, dict[str, float]]:
+    """Get weather metrics for multiple cities
+
+    Returns a dictionary mapping city names to their metrics
+    """
+    # Returns nested dictionaries with weather metrics
+    return {
+        city: {"temperature": 20.0 + i * 2, "humidity": 60.0 + i * 5, "pressure": 1013.0 + i * 0.5}
+        for i, city in enumerate(cities)
+    }
+
+
+# Example 4: Using dataclass for weather alerts
+@dataclass
+class WeatherAlert:
+    """Weather alert information"""
+
+    severity: str  # "low", "medium", "high"
+    title: str
+    description: str
+    affected_areas: list[str]
+    valid_until: datetime
+
+
+@mcp.tool()
+def get_weather_alerts(region: str) -> list[WeatherAlert]:
+    """Get active weather alerts for a region"""
+    # In production, this would fetch real alerts
+    if region.lower() == "california":
+        return [
+            WeatherAlert(
+                severity="high",
+                title="Heat Wave Warning",
+                description="Temperatures expected to exceed 40°C",
+                affected_areas=["Los Angeles", "San Diego", "Riverside"],
+                valid_until=datetime(2024, 7, 15, 18, 0),
+            ),
+            WeatherAlert(
+                severity="medium",
+                title="Air Quality Advisory",
+                description="Poor air quality due to wildfire smoke",
+                affected_areas=["San Francisco Bay Area"],
+                valid_until=datetime(2024, 7, 14, 12, 0),
+            ),
+        ]
+    return []
+
+
+# Example 5: Returning primitives with structured output
+@mcp.tool()
+def get_temperature(city: str, unit: str = "celsius") -> float:
+    """Get just the temperature for a city
+
+    When returning primitives as structured output,
+    the result is wrapped in {"result": value}
+    """
+    base_temp = 22.5
+    if unit.lower() == "fahrenheit":
+        return base_temp * 9 / 5 + 32
+    return base_temp
+
+
+# Example 6: Weather statistics with nested models
+class DailyStats(BaseModel):
+    """Statistics for a single day"""
+
+    high: float
+    low: float
+    mean: float
+
+
+class WeatherStats(BaseModel):
+    """Weather statistics over a period"""
+
+    location: str
+    period_days: int
+    temperature: DailyStats
+    humidity: DailyStats
+    precipitation_mm: float = Field(description="Total precipitation in millimeters")
+
+
+@mcp.tool()
+def get_weather_stats(city: str, days: int = 7) -> WeatherStats:
+    """Get weather statistics for the past N days"""
+    return WeatherStats(
+        location=city,
+        period_days=days,
+        temperature=DailyStats(high=28.5, low=15.2, mean=21.8),
+        humidity=DailyStats(high=85.0, low=45.0, mean=65.0),
+        precipitation_mm=12.4,
+    )
+
+
+if __name__ == "__main__":
+
+    async def test() -> None:
+        """Test the tools by calling them through the server as a client would"""
+        print("Testing Weather Service Tools (via MCP protocol)\n")
+        print("=" * 80)
+
+        async with client_session(mcp._mcp_server) as client:
+            # Test get_weather
+            result = await client.call_tool("get_weather", {"city": "London"})
+            print("\nWeather in London:")
+            print(json.dumps(result.structuredContent, indent=2))
+
+            # Test get_weather_summary
+            result = await client.call_tool("get_weather_summary", {"city": "Paris"})
+            print("\nWeather summary for Paris:")
+            print(json.dumps(result.structuredContent, indent=2))
+
+            # Test get_weather_metrics
+            result = await client.call_tool("get_weather_metrics", {"cities": ["Tokyo", "Sydney", "Mumbai"]})
+            print("\nWeather metrics:")
+            print(json.dumps(result.structuredContent, indent=2))
+
+            # Test get_weather_alerts
+            result = await client.call_tool("get_weather_alerts", {"region": "California"})
+            print("\nWeather alerts for California:")
+            print(json.dumps(result.structuredContent, indent=2))
+
+            # Test get_temperature
+            result = await client.call_tool("get_temperature", {"city": "Berlin", "unit": "fahrenheit"})
+            print("\nTemperature in Berlin:")
+            print(json.dumps(result.structuredContent, indent=2))
+
+            # Test get_weather_stats
+            result = await client.call_tool("get_weather_stats", {"city": "Seattle", "days": 30})
+            print("\nWeather stats for Seattle (30 days):")
+            print(json.dumps(result.structuredContent, indent=2))
+
+            # Also show the text content for comparison
+            print("\nText content for last result:")
+            for content in result.content:
+                if content.type == "text":
+                    print(content.text)
+
+    async def print_schemas() -> None:
+        """Print all tool schemas"""
+        print("Tool Schemas for Weather Service\n")
+        print("=" * 80)
+
+        tools = await mcp.list_tools()
+        for tool in tools:
+            print(f"\nTool: {tool.name}")
+            print(f"Description: {tool.description}")
+            print("Input Schema:")
+            print(json.dumps(tool.inputSchema, indent=2))
+
+            if tool.outputSchema:
+                print("Output Schema:")
+                print(json.dumps(tool.outputSchema, indent=2))
+            else:
+                print("Output Schema: None (returns unstructured content)")
+
+            print("-" * 80)
+
+    # Check command line arguments
+    if len(sys.argv) > 1 and sys.argv[1] == "--schemas":
+        asyncio.run(print_schemas())
+    else:
+        print("Usage:")
+        print("  python weather_structured.py          # Run tool tests")
+        print("  python weather_structured.py --schemas # Print tool schemas")
+        print()
+        asyncio.run(test())