Tools

Tools are the core building blocks that allow your LLM to interact with external systems, execute code, and access data that isn’t in its training data. In FastMCP, tools are Python functions exposed to LLMs through the MCP protocol.

​

What Are Tools?

Tools in FastMCP transform regular Python functions into capabilities that LLMs can invoke during conversations. When an LLM decides to use a tool:

1. It sends a request with parameters based on the tool’s schema.
2. FastMCP validates these parameters against your function’s signature.
3. Your function executes with the validated inputs.
4. The result is returned to the LLM, which can use it in its response.

This allows LLMs to perform tasks like querying databases, calling APIs, making calculations, or accessing files—extending their capabilities beyond what’s in their training data.

​

Tools

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The @tool Decorator

Creating a tool is as simple as decorating a Python function with @mcp.tool:

from fastmcp import FastMCP

mcp = FastMCP(name="CalculatorServer")

@mcp.tool
def add(a: int, b: int) -> int:
    """Adds two integer numbers together."""
    return a + b

When this tool is registered, FastMCP automatically:

• Uses the function name (add) as the tool name.
• Uses the function’s docstring (Adds two integer numbers...) as the tool description.
• Generates an input schema based on the function’s parameters and type annotations.
• Handles parameter validation and error reporting.

The way you define your Python function dictates how the tool appears and behaves for the LLM client.

​

Decorator Arguments

While FastMCP infers the name and description from your function, you can override these and add additional metadata using arguments to the @mcp.tool decorator:

@mcp.tool(
    name="find_products",           # Custom tool name for the LLM
    description="Search the product catalog with optional category filtering.", # Custom description
    tags={"catalog", "search"},      # Optional tags for organization/filtering
)
def search_products_implementation(query: str, category: str | None = None) -> list[dict]:
    """Internal function description (ignored if description is provided above)."""
    # Implementation...
    print(f"Searching for '{query}' in category '{category}'")
    return [{"id": 2, "name": "Another Product"}]

​

Tool Parameters

​

Type Annotations

Type annotations for parameters are essential for proper tool functionality. They:

1. Inform the LLM about the expected data types for each parameter
2. Enable FastMCP to validate input data from clients
3. Generate accurate JSON schemas for the MCP protocol

Use standard Python type annotations for parameters:

@mcp.tool
def analyze_text(
    text: str,
    max_tokens: int = 100,
    language: str | None = None
) -> dict:
    """Analyze the provided text."""
    # Implementation...

​

Parameter Metadata

You can provide additional metadata about parameters using Pydantic’s Field class with Annotated. This approach is preferred as it’s more modern and keeps type hints separate from validation rules:

from typing import Annotated
from pydantic import Field

@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,
    width: Annotated[int, Field(description="Target width in pixels", ge=1, le=2000)] = 800,
    format: Annotated[
        Literal["jpeg", "png", "webp"], 
        Field(description="Output image format")
    ] = "jpeg"
) -> dict:
    """Process an image with optional resizing."""
    # Implementation...

You can also use the Field as a default value, though the Annotated approach is preferred:

@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)
) -> list:
    """Search the database with the provided query."""
    # Implementation...

Field provides several validation and documentation features:

• description: Human-readable explanation of the parameter (shown to LLMs)
• ge/gt/le/lt: Greater/less than (or equal) constraints
• min_length/max_length: String or collection length constraints
• pattern: Regex pattern for string validation
• default: Default value if parameter is omitted

​

Supported Types

FastMCP supports a wide range of type annotations, including all Pydantic types:

For additional type annotations not listed here, see the Parameter Types section below for more detailed information and examples.

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Optional Arguments

FastMCP follows Python’s standard function parameter conventions. Parameters without default values are required, while those with default values are optional.

@mcp.tool
def search_products(
    query: str,                   # Required - no default value
    max_results: int = 10,        # Optional - has default value
    sort_by: str = "relevance",   # Optional - has default value
    category: str | None = None   # Optional - can be None
) -> list[dict]:
    """Search the product catalog."""
    # Implementation...

In this example, the LLM must provide a query parameter, while max_results, sort_by, and category will use their default values if not explicitly provided.

​

Excluding Arguments

You can exclude certain arguments from the tool schema shown to the LLM. This is useful for arguments that are injected at runtime (such as state, user_id, or credentials) and should not be exposed to the LLM or client. Only arguments with default values can be excluded; attempting to exclude a required argument will raise an error.

Example:

@mcp.tool(
    name="get_user_details",
    exclude_args=["user_id"]
)
def get_user_details(user_id: str = None) -> str:
    # user_id will be injected by the server, not provided by the LLM
    ...

With this configuration, user_id will not appear in the tool’s parameter schema, but can still be set by the server or framework at runtime.

For more complex tool transformations, see Transforming Tools.

​

Disabling Tools

You can control the visibility and availability of tools by enabling or disabling them. This is useful for feature flagging, maintenance, or dynamically changing the toolset available to a client. Disabled tools will not appear in the list of available tools returned by list_tools, and attempting to call a disabled tool will result in an “Unknown tool” error, just as if the tool did not exist.

By default, all tools are enabled. You can disable a tool upon creation using the enabled parameter in the decorator:

@mcp.tool(enabled=False)
def maintenance_tool():
    """This tool is currently under maintenance."""
    return "This tool is disabled."

You can also toggle a tool’s state programmatically after it has been created:

@mcp.tool
def dynamic_tool():
    return "I am a dynamic tool."

# Disable and re-enable the tool
dynamic_tool.disable()
dynamic_tool.enable()

​

Async Tools

FastMCP seamlessly supports both standard (def) and asynchronous (async def) functions as tools.

# Synchronous tool (suitable for CPU-bound or quick tasks)
@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
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.
    # This prevents blocking the server while waiting for external operations.
    async with aiohttp.ClientSession() as session:
        async with session.get(f"https://api.example.com/weather/{city}") as response:
            # Check response status before returning
            response.raise_for_status()
            return await response.json()

Use async def when your tool needs to perform operations that might wait for external systems (network requests, database queries, file access) to keep your server responsive.

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Return Values

FastMCP tools can return data in two complementary formats: traditional content blocks (like text and images) and structured outputs (machine-readable JSON). When you add return type annotations, FastMCP automatically generates output schemas to validate the structured data and enables clients to deserialize results back to Python objects.

Understanding how these three concepts work together:

• Return Values: What your Python function returns (determines both content blocks and structured data)
• Structured Outputs: JSON data sent alongside traditional content for machine processing
• Output Schemas: JSON Schema declarations that describe and validate the structured output format

The following sections explain each concept in detail.

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Content Blocks

FastMCP automatically converts tool return values into appropriate MCP content blocks:

• str: Sent as TextContent
• bytes: Base64 encoded and sent as BlobResourceContents (within an EmbeddedResource)
• fastmcp.utilities.types.Image: Sent as ImageContent
• fastmcp.utilities.types.Audio: Sent as AudioContent
• fastmcp.utilities.types.File: Sent as base64-encoded EmbeddedResource
• A list of any of the above: Converts each item appropriately
• None: Results in an empty response

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Structured Output

The 6/18/2025 MCP spec update introduced structured content, which is a new way to return data from tools. Structured content is a JSON object that is sent alongside traditional content. FastMCP automatically creates structured outputs alongside traditional content when your tool returns data that has a JSON object representation. This provides machine-readable JSON data that clients can deserialize back to Python objects.

Automatic Structured Content Rules:

• Object-like results (dict, Pydantic models, dataclasses) → Always become structured content (even without output schema)
• Non-object results (int, str, list) → Only become structured content if there’s an output schema to validate/serialize them
• All results → Always become traditional content blocks for backward compatibility

Object-like Results (Automatic Structured Content)

@mcp.tool
def get_user_data(user_id: str) -> dict:
    """Get user data without type annotation."""
    return {"name": "Alice", "age": 30, "active": True}

Non-object Results (Schema Required)

@mcp.tool  
def calculate_sum(a: int, b: int):
    """Calculate sum without return annotation."""
    return a + b  # Returns 8

Complex Type Example

from dataclasses import dataclass
from fastmcp import FastMCP

mcp = FastMCP()

@dataclass
class Person:
    name: str
    age: int
    email: str

@mcp.tool
def get_user_profile(user_id: str) -> Person:
    """Get a user's profile information."""
    return Person(name="Alice", age=30, email="alice@example.com")

​

Output Schemas

The 6/18/2025 MCP spec update introduced output schemas, which are a new way to describe the expected output format of a tool. When an output schema is provided, the tool must return structured output that matches the schema.

When you add return type annotations to your functions, FastMCP automatically generates JSON schemas that describe the expected output format. These schemas help MCP clients understand and validate the structured data they receive.

Primitive Type Wrapping

For primitive return types (like int, str, bool), FastMCP automatically wraps the result under a "result" key to create valid structured output:

@mcp.tool
def calculate_sum(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

Manual Schema Control

You can override the automatically generated schema by providing a custom output_schema:

@mcp.tool(output_schema={
    "type": "object", 
    "properties": {
        "data": {"type": "string"},
        "metadata": {"type": "object"}
    }
})
def custom_schema_tool() -> dict:
    """Tool with custom output schema."""
    return {"data": "Hello", "metadata": {"version": "1.0"}}

Schema generation works for most common types including basic types, collections, union types, Pydantic models, TypedDict structures, and dataclasses.

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Full Control with ToolResult

For complete control over both traditional content and structured output, return a ToolResult object:

from fastmcp.tools.tool import ToolResult

@mcp.tool
def advanced_tool() -> ToolResult:
    """Tool with full control over output."""
    return ToolResult(
        content=[TextContent(text="Human-readable summary")],
        structured_content={"data": "value", "count": 42}
    )

When returning ToolResult:

• You control exactly what content and structured data is sent
• Output schemas are optional - structured content can be provided without a schema
• Clients receive both traditional content blocks and structured data

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Error Handling

If your tool encounters an error, you can raise a standard Python exception (ValueError, TypeError, FileNotFoundError, custom exceptions, etc.) or a FastMCP ToolError.

By default, all exceptions (including their details) are logged and converted into an MCP error response to be sent back to the client LLM. This helps the LLM understand failures and react appropriately.

If you want to mask internal error details for security reasons, you can:

1. Use the mask_error_details=True parameter when creating your FastMCP instance:

mcp = FastMCP(name="SecureServer", mask_error_details=True)

2. Or use ToolError to explicitly control what error information is sent to clients:

from fastmcp import FastMCP
from fastmcp.exceptions import ToolError

@mcp.tool
def divide(a: float, b: float) -> float:
    """Divide a by b."""

    if b == 0:
        # Error messages from ToolError are always sent to clients,
        # regardless of mask_error_details setting
        raise ToolError("Division by zero is not allowed.")
    
    # If mask_error_details=True, this message would be masked
    if not isinstance(a, (int, float)) or not isinstance(b, (int, float)):
        raise TypeError("Both arguments must be numbers.")
        
    return a / b

When mask_error_details=True, only error messages from ToolError will include details, other exceptions will be converted to a generic message.

​

Annotations

FastMCP allows you to add specialized metadata to your tools through annotations. These annotations communicate how tools behave to client applications without consuming token context in LLM prompts.

Annotations serve several purposes in client applications:

• Adding user-friendly titles for display purposes
• Indicating whether tools modify data or systems
• Describing the safety profile of tools (destructive vs. non-destructive)
• Signaling if tools interact with external systems

You can add annotations to a tool using the annotations parameter in the @mcp.tool decorator:

@mcp.tool(
    annotations={
        "title": "Calculate Sum",
        "readOnlyHint": True,
        "openWorldHint": False
    }
)
def calculate_sum(a: float, b: float) -> float:
    """Add two numbers together."""
    return a + b

FastMCP supports these standard annotations:

Remember that annotations help make better user experiences but should be treated as advisory hints. They help client applications present appropriate UI elements and safety controls, but won’t enforce security boundaries on their own. Always focus on making your annotations accurately represent what your tool actually does.

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Notifications

FastMCP automatically sends notifications/tools/list_changed notifications to connected clients when tools are added, removed, enabled, or disabled. This allows clients to stay up-to-date with the current tool set without manually polling for changes.

@mcp.tool
def example_tool() -> str:
    return "Hello!"

# These operations trigger notifications:
mcp.add_tool(example_tool)     # Sends tools/list_changed notification
example_tool.disable()         # Sends tools/list_changed notification  
example_tool.enable()          # Sends tools/list_changed notification
mcp.remove_tool("example_tool") # Sends tools/list_changed notification

Notifications are only sent when these operations occur within an active MCP request context (e.g., when called from within a tool or other MCP operation). Operations performed during server initialization do not trigger notifications.

Clients can handle these notifications using a message handler to automatically refresh their tool lists or update their interfaces.

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MCP Context

Tools can access MCP features like logging, reading resources, or reporting progress through the Context object. To use it, add a parameter to your tool function with the type hint Context.

from fastmcp import FastMCP, Context

mcp = FastMCP(name="ContextDemo")

@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}")
    
    # Read a resource
    resource = await ctx.read_resource(data_uri)
    data = resource[0].content if resource else ""
    
    # Report progress
    await ctx.report_progress(progress=50, total=100)
    
    # Example request to the client's LLM for help
    summary = await ctx.sample(f"Summarize this in 10 words: {data[:200]}")
    
    await ctx.report_progress(progress=100, total=100)
    return {
        "length": len(data),
        "summary": summary.text
    }

The Context object provides access to:

• Logging: ctx.debug(), ctx.info(), ctx.warning(), ctx.error()
• Progress Reporting: ctx.report_progress(progress, total)
• Resource Access: ctx.read_resource(uri)
• LLM Sampling: ctx.sample(...)
• Request Information: ctx.request_id, ctx.client_id

For full documentation on the Context object and all its capabilities, see the Context documentation.

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Parameter Types

FastMCP supports a wide variety of parameter types to give you flexibility when designing your tools.

FastMCP generally supports all types that Pydantic supports as fields, including all Pydantic custom types. This means you can use any type that can be validated and parsed by Pydantic in your tool parameters.

FastMCP supports type coercion when possible. This means that if a client sends data that doesn’t match the expected type, FastMCP will attempt to convert it to the appropriate type. For example, if a client sends a string for a parameter annotated as int, FastMCP will attempt to convert it to an integer. If the conversion is not possible, FastMCP will return a validation error.

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Built-in Types

The most common parameter types are Python’s built-in scalar types:

@mcp.tool
def process_values(
    name: str,             # Text data
    count: int,            # Integer numbers
    amount: float,         # Floating point numbers
    enabled: bool          # Boolean values (True/False)
):
    """Process various value types."""
    # Implementation...

These types provide clear expectations to the LLM about what values are acceptable and allow FastMCP to validate inputs properly. Even if a client provides a string like “42”, it will be coerced to an integer for parameters annotated as int.

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Date and Time Types

FastMCP supports various date and time types from the datetime module:

from datetime import datetime, date, timedelta

@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
    duration: timedelta = timedelta(hours=1)  # Integer seconds or timedelta
) -> str:
    """Process date and time information."""
    # Types are automatically converted from strings
    assert isinstance(event_date, date)  
    assert isinstance(event_time, datetime)
    assert isinstance(duration, timedelta)
    
    return f"Event on {event_date} at {event_time} for {duration}"

• datetime - Accepts ISO format strings (e.g., “2023-04-15T14:30:00”)
• date - Accepts ISO format date strings (e.g., “2023-04-15”)
• timedelta - Accepts integer seconds or timedelta objects

​

Collection Types

FastMCP supports all standard Python collection types:

@mcp.tool
def analyze_data(
    values: list[float],           # List of numbers
    properties: dict[str, str],    # Dictionary with string keys and values
    unique_ids: set[int],          # Set of unique integers
    coordinates: tuple[float, float],  # Tuple with fixed structure
    mixed_data: dict[str, list[int]] # Nested collections
):
    """Analyze collections of data."""
    # Implementation...

All collection types can be used as parameter annotations:

• list[T] - Ordered sequence of items
• dict[K, V] - Key-value mapping
• set[T] - Unordered collection of unique items
• tuple[T1, T2, ...] - Fixed-length sequence with potentially different types

Collection types can be nested and combined to represent complex data structures. JSON strings that match the expected structure will be automatically parsed and converted to the appropriate Python collection type.

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Union and Optional Types

For parameters that can accept multiple types or may be omitted:

@mcp.tool
def flexible_search(
    query: str | int,              # Can be either string or integer
    filters: dict[str, str] | None = None,  # Optional dictionary
    sort_field: str | None = None  # Optional string
):
    """Search with flexible parameter types."""
    # Implementation...

Modern Python syntax (str | int) is preferred over older Union[str, int] forms. Similarly, str | None is preferred over Optional[str].

​

Constrained Types

When a parameter must be one of a predefined set of values, you can use either Literal types or Enums:

​

Literals

Literals constrain parameters to a specific set of values:

from typing import Literal

@mcp.tool
def sort_data(
    data: list[float],
    order: Literal["ascending", "descending"] = "ascending",
    algorithm: Literal["quicksort", "mergesort", "heapsort"] = "quicksort"
):
    """Sort data using specific options."""
    # Implementation...

Literal types:

• Specify exact allowable values directly in the type annotation
• Help LLMs understand exactly which values are acceptable
• Provide input validation (errors for invalid values)
• Create clear schemas for clients

​

Enums

For more structured sets of constrained values, use Python’s Enum class:

from enum import Enum

class Color(Enum):
    RED = "red"
    GREEN = "green"
    BLUE = "blue"

@mcp.tool
def process_image(
    image_path: str, 
    color_filter: Color = Color.RED
):
    """Process an image with a color filter."""
    # Implementation...
    # color_filter will be a Color enum member

When using Enum types:

• Clients should provide the enum’s value (e.g., “red”), not the enum member name (e.g., “RED”)
• FastMCP automatically coerces the string value into the appropriate Enum object
• Your function receives the actual Enum member (e.g., Color.RED)
• Validation errors are raised for values not in the enum

​

Binary Data

There are two approaches to handling binary data in tool parameters:

​

Bytes

@mcp.tool
def process_binary(data: bytes):
    """Process binary data directly.
    
    The client can send a binary string, which will be 
    converted directly to bytes.
    """
    # Implementation using binary data
    data_length = len(data)
    # ...

When you annotate a parameter as bytes, FastMCP will:

• Convert raw strings directly to bytes
• Validate that the input can be properly represented as bytes

FastMCP does not automatically decode base64-encoded strings for bytes parameters. If you need to accept base64-encoded data, you should handle the decoding manually as shown below.

​

Base64-encoded strings

from typing import Annotated
from pydantic import Field

@mcp.tool
def process_image_data(
    image_data: Annotated[str, Field(description="Base64-encoded image data")]
):
    """Process an image from base64-encoded string.
    
    The client is expected to provide base64-encoded data as a string.
    You'll need to decode it manually.
    """
    # Manual base64 decoding
    import base64
    binary_data = base64.b64decode(image_data)
    # Process binary_data...

This approach is recommended when you expect to receive base64-encoded binary data from clients.

​

Paths

The Path type from the pathlib module can be used for file system paths:

from pathlib import Path

@mcp.tool
def process_file(path: Path) -> str:
    """Process a file at the given path."""
    assert isinstance(path, Path)  # Path is properly converted
    return f"Processing file at {path}"

When a client sends a string path, FastMCP automatically converts it to a Path object.

​

UUIDs

The UUID type from the uuid module can be used for unique identifiers:

import uuid

@mcp.tool
def process_item(
    item_id: uuid.UUID  # String UUID or UUID object
) -> str:
    """Process an item with the given UUID."""
    assert isinstance(item_id, uuid.UUID)  # Properly converted to UUID
    return f"Processing item {item_id}"

When a client sends a string UUID (e.g., “123e4567-e89b-12d3-a456-426614174000”), FastMCP automatically converts it to a UUID object.

​

Pydantic Models

For complex, structured data with nested fields and validation, use Pydantic models:

from pydantic import BaseModel, Field
from typing import Optional

class User(BaseModel):
    username: str
    email: str = Field(description="User's email address")
    age: int | None = None
    is_active: bool = True

@mcp.tool
def create_user(user: User):
    """Create a new user in the system."""
    # The input is automatically validated against the User model
    # Even if provided as a JSON string or dict
    # Implementation...

Using Pydantic models provides:

• Clear, self-documenting structure for complex inputs
• Built-in data validation
• Automatic generation of detailed JSON schemas for the LLM
• Automatic conversion from dict/JSON input

Clients can provide data for Pydantic model parameters as either:

• A JSON object (string)
• A dictionary with the appropriate structure
• Nested parameters in the appropriate format

​

Pydantic Fields

FastMCP supports robust parameter validation through Pydantic’s Field class. This is especially useful to ensure that input values meet specific requirements beyond just their type.

Note that fields can be used outside Pydantic models to provide metadata and validation constraints. The preferred approach is using Annotated with Field:

from typing import Annotated
from pydantic import Field

@mcp.tool
def analyze_metrics(
    # Numbers with range constraints
    count: Annotated[int, Field(ge=0, le=100)],         # 0 <= count <= 100
    ratio: Annotated[float, Field(gt=0, lt=1.0)],       # 0 < ratio < 1.0
    
    # String with pattern and length constraints
    user_id: Annotated[str, Field(
        pattern=r"^[A-Z]{2}\d{4}$",                     # Must match regex pattern
        description="User ID in format XX0000"
    )],
    
    # String with length constraints
    comment: Annotated[str, Field(min_length=3, max_length=500)] = "",
    
    # Numeric constraints
    factor: Annotated[int, Field(multiple_of=5)] = 10,  # Must be multiple of 5
):
    """Analyze metrics with validated parameters."""
    # Implementation...

You can also use Field as a default value, though the Annotated approach is preferred:

@mcp.tool
def validate_data(
    # Value constraints
    age: int = Field(ge=0, lt=120),                     # 0 <= age < 120
    
    # String constraints
    email: str = Field(pattern=r"^[\w\.-]+@[\w\.-]+\.\w+$"),  # Email pattern
    
    # Collection constraints
    tags: list[str] = Field(min_length=1, max_length=10)  # 1-10 tags
):
    """Process data with field validations."""
    # Implementation...

Common validation options include:

When a client sends invalid data, FastMCP will return a validation error explaining why the parameter failed validation.

​

Server Behavior

​

Duplicate Tools

You can control how the FastMCP server behaves if you try to register multiple tools with the same name. This is configured using the on_duplicate_tools argument when creating the FastMCP instance.

from fastmcp import FastMCP

mcp = FastMCP(
    name="StrictServer",
    # Configure behavior for duplicate tool names
    on_duplicate_tools="error"
)

@mcp.tool
def my_tool(): return "Version 1"

# This will now raise a ValueError because 'my_tool' already exists
# and on_duplicate_tools is set to "error".
# @mcp.tool
# def my_tool(): return "Version 2"

The duplicate behavior options are:

• "warn" (default): Logs a warning and the new tool replaces the old one.
• "error": Raises a ValueError, preventing the duplicate registration.
• "replace": Silently replaces the existing tool with the new one.
• "ignore": Keeps the original tool and ignores the new registration attempt.

​

Removing Tools

You can dynamically remove tools from a server using the remove_tool method:

from fastmcp import FastMCP

mcp = FastMCP(name="DynamicToolServer")

@mcp.tool
def calculate_sum(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

mcp.remove_tool("calculate_sum")