Tools - FastMCP

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

It sends a request with parameters based on the tool’s schema.
FastMCP validates these parameters against your function’s signature.
Your function executes with the validated inputs.
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.

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.
Parses the function’s docstring for the tool description and, if present, per-parameter descriptions (see Docstring Descriptions).
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.

Functions with *args or **kwargs are not supported as tools. This restriction exists because FastMCP needs to generate a complete parameter schema for the MCP protocol, which isn’t possible with variable argument lists.

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
    meta={"version": "1.2", "author": "product-team"}  # Custom metadata
)
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 Decorator Arguments

name

str | None

Sets the explicit tool name exposed via MCP. If not provided, uses the function name

description

str | None

Provides the description exposed via MCP. If set, the function’s docstring is ignored for the tool description, though docstring-derived parameter descriptions still apply (see Docstring Descriptions).

Using with Methods

The @mcp.tool decorator registers tools immediately, which doesn’t work with instance or class methods (you’d see self or cls as required parameters). For methods, use the standalone @tool decorator to attach metadata, then register the bound method:

from fastmcp import FastMCP
from fastmcp.tools import tool

class Calculator:
    def __init__(self, multiplier: int):
        self.multiplier = multiplier

    @tool()
    def multiply(self, x: int) -> int:
        """Multiply x by the instance multiplier."""
        return x * self.multiplier

calc = Calculator(multiplier=3)
mcp = FastMCP()
mcp.add_tool(calc.multiply)  # Registers with correct schema (only 'x', not 'self')

Async Support

FastMCP supports both asynchronous (async def) and synchronous (def) functions as tools. Synchronous tools automatically run in a threadpool to avoid blocking the event loop, so multiple tool calls can execute concurrently even if individual tools perform blocking operations.

from fastmcp import FastMCP
import time

mcp = FastMCP()

@mcp.tool
def slow_tool(x: int) -> int:
    """This sync function won't block other concurrent requests."""
    time.sleep(2)  # Runs in threadpool, not on the event loop
    return x * 2

For I/O-bound operations like network requests or database queries, async tools are still preferred since they’re more efficient than threadpool dispatch. Use sync tools when working with synchronous libraries or for simple operations where the threading overhead doesn’t matter.

Thread affinity

This section applies to sync tools only. Async tools already run on the event loop and are not affected. Some libraries bind state to the thread they’re first used from and break when called from a different thread. The most common case is Windows COM — libraries like uiautomation, comtypes, and parts of pywin32 require CoInitialize to have been called on the current thread, and worker-pool threads don’t initialize COM by default. Similar constraints apply to tkinter, some GPU bindings (CUDA contexts), and certain hardware drivers. For these cases, pass run_in_thread=False so FastMCP invokes the sync function inline on the event loop thread instead of dispatching it to a worker:

import uiautomation as auto

@mcp.tool(run_in_thread=False)
def list_windows() -> list[str]:
    """List desktop windows via Windows UI Automation (COM)."""
    desktop = auto.GetRootControl()
    return [w.Name for w in desktop.GetChildren()[:5]]

The tradeoff is that the event loop is blocked for the duration of the call — other in-flight requests wait until the tool returns. Keep run_in_thread=False reserved for tools that genuinely need thread affinity, and prefer short-running calls in that path. Inline sync calls have no cancellation checkpoints, so timeout cannot interrupt them. Combining timeout with run_in_thread=False on a sync function is rejected at registration — drop one or the other.

Arguments

By default, FastMCP converts Python functions into MCP tools by inspecting the function’s signature and type annotations. This allows you to use standard Python type annotations for your tools. In general, the framework strives to “just work”: idiomatic Python behaviors like parameter defaults and type annotations are automatically translated into MCP schemas. However, there are a number of ways to customize the behavior of your tools.

FastMCP automatically dereferences $ref entries in tool schemas to ensure compatibility with MCP clients that don’t fully support JSON Schema references (e.g., VS Code Copilot, Claude Desktop). This means complex Pydantic models with shared types are inlined in the schema rather than using $defs references.Dereferencing happens at serve-time via middleware, so your schemas are stored with $ref intact and only inlined when sent to clients. If you know your clients handle $ref correctly and prefer smaller schemas, you can opt out:

mcp = FastMCP("my-server", dereference_schemas=False)

Type Annotations

MCP tools have typed arguments, and FastMCP uses type annotations to determine those types. Therefore, you should use standard Python type annotations for tool arguments:

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

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

Type Annotation	Example	Description
Basic types	`int`, `float`, `str`, `bool`	Simple scalar values
Binary data	`bytes`	Binary content (raw strings, not auto-decoded base64)
Date and Time	`datetime`, `date`, `timedelta`	Date and time objects (ISO format strings)
Collection types	`list[str]`, `dict[str, int]`, `set[int]`	Collections of items
Optional types	`float \| None`, `Optional[float]`	Parameters that may be null/omitted
Union types	`str \| int`, `Union[str, int]`	Parameters accepting multiple types
Constrained types	`Literal["A", "B"]`, `Enum`	Parameters with specific allowed values
Paths	`Path`	File system paths (auto-converted from strings)
UUIDs	`UUID`	Universally unique identifiers (auto-converted from strings)
Pydantic models	`UserData`	Complex structured data with validation

FastMCP supports all types that Pydantic supports as fields, including all Pydantic custom types. A few FastMCP-specific behaviors to note: Binary Data: bytes parameters accept raw strings without automatic base64 decoding. For base64 data, use str and decode manually with base64.b64decode(). Enums: Clients send enum values ("red"), not names ("RED"). Your function receives the Enum member (Color.RED). Paths and UUIDs: String inputs are automatically converted to Path and UUID objects. Pydantic Models: Must be provided as JSON objects (dicts), not stringified JSON. Even with flexible validation, {"user": {"name": "Alice"}} works, but {"user": '{"name": "Alice"}'} does not.

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.

Validation Modes

By default, FastMCP uses Pydantic’s flexible validation that coerces compatible inputs to match your type annotations. This improves compatibility with LLM clients that may send string representations of values (like "10" for an integer parameter). If you need stricter validation that rejects any type mismatches, you can enable strict input validation. Strict mode uses the MCP SDK’s built-in JSON Schema validation to validate inputs against the exact schema before passing them to your function:

# Enable strict validation for this server
mcp = FastMCP("StrictServer", strict_input_validation=True)

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

# With strict_input_validation=True, sending {"a": "10", "b": "20"} will fail
# With strict_input_validation=False (default), it will be coerced to integers

Validation Behavior Comparison:

Input Type	strict_input_validation=False (default)	strict_input_validation=True
String integers (`"10"` for `int`)	✅ Coerced to integer	❌ Validation error
String floats (`"3.14"` for `float`)	✅ Coerced to float	❌ Validation error
String booleans (`"true"` for `bool`)	✅ Coerced to boolean	❌ Validation error
Lists with string elements (`["1", "2"]` for `list[int]`)	✅ Elements coerced	❌ Validation error
Pydantic model fields with type mismatches	✅ Fields coerced	❌ Validation error
Invalid values (`"abc"` for `int`)	❌ Validation error	❌ Validation error

Note on Pydantic Models: Even with strict_input_validation=False, Pydantic model parameters must be provided as JSON objects (dicts), not as stringified JSON. For example, {"user": {"name": "Alice"}} works, but {"user": '{"name": "Alice"}'} does not.

The default flexible validation mode is recommended for most use cases as it handles common LLM client behaviors gracefully while still providing strong type safety through Pydantic’s validation.

Parameter Metadata

You can provide additional metadata about parameters in several ways:

Docstring Descriptions

FastMCP parses your function’s docstring to extract both the tool description and per-parameter descriptions. Google, NumPy, and Sphinx docstring styles are all supported — the parser tries each and uses whichever finds parameter descriptions:

@mcp.tool
def process_image(
    image_url: str,
    resize: bool = False,
    width: int = 800,
) -> dict:
    """Process an image with optional resizing.

    Args:
        image_url: URL of the image to process.
        resize: Whether to resize the image.
        width: Target width in pixels.
    """
    # Implementation...

The free-form text above the Args section — whether a single line or multiple paragraphs — becomes the tool description, and each parameter’s docstring entry becomes the description for that parameter in the generated schema. Sections like Returns, Raises, and Example are excluded from the description but otherwise ignored. If a parameter already has an explicit description — via Annotated[x, "..."] or Field(description=...) — that description takes precedence over the docstring. This makes it safe to adopt docstring-based descriptions incrementally: existing annotations keep working, and docstrings fill in the gaps.

Simple String Descriptions

For basic parameter descriptions, you can use a convenient shorthand with Annotated:

from typing import Annotated

@mcp.tool
def process_image(
    image_url: Annotated[str, "URL of the image to process"],
    resize: Annotated[bool, "Whether to resize the image"] = False,
    width: Annotated[int, "Target width in pixels"] = 800,
    format: Annotated[str, "Output image format"] = "jpeg"
) -> dict:
    """Process an image with optional resizing."""
    # Implementation...

This shorthand syntax is equivalent to using Field(description=...) but more concise for simple descriptions.

This shorthand syntax is only applied to Annotated types with a single string description.

Advanced Metadata with Field

For validation constraints and advanced metadata, use Pydantic’s Field class with Annotated:

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

Hiding Parameters from the LLM

To inject values at runtime without exposing them to the LLM (such as user_id, credentials, or database connections), use dependency injection with Depends(). Parameters using Depends() are automatically excluded from the tool schema:

from fastmcp import FastMCP
from fastmcp.dependencies import Depends

mcp = FastMCP()

def get_user_id() -> str:
    return "user_123"  # Injected at runtime

@mcp.tool
def get_user_details(user_id: str = Depends(get_user_id)) -> str:
    # user_id is injected by the server, not provided by the LLM
    return f"Details for {user_id}"

See Custom Dependencies for more details on dependency injection.

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.

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
MCP SDK content blocks: Sent as-is
A list of any of the above: Converts each item according to the above rules
None: Results in an empty response

Media Helper Classes

FastMCP provides helper classes for returning images, audio, and files. When you return one of these classes, either directly or as part of a list, FastMCP automatically converts it to the appropriate MCP content block. For example, if you return a fastmcp.utilities.types.Image object, FastMCP will convert it to an MCP ImageContent block with the correct MIME type and base64 encoding.

from fastmcp.utilities.types import Image, Audio, File

@mcp.tool
def get_chart() -> Image:
    """Generate a chart image."""
    return Image(path="chart.png")

@mcp.tool
def get_multiple_charts() -> list[Image]:
    """Return multiple charts."""
    return [Image(path="chart1.png"), Image(path="chart2.png")]

Helper classes are only automatically converted to MCP content blocks when returned directly or as part of a list. For more complex containers like dicts, you can manually convert them to MCP types:

# ✅ Automatic conversion
return Image(path="chart.png")
return [Image(path="chart1.png"), "text content"]

# ❌ Will not be automatically converted
return {"image": Image(path="chart.png")}

# ✅ Manual conversion for nested use
return {"image": Image(path="chart.png").to_image_content()}

Each helper class accepts either path= or data= (mutually exclusive):

path: File path (string or Path object) - MIME type detected from extension
data: Raw bytes - requires format= parameter for MIME type
format: Optional format override (e.g., “png”, “wav”, “pdf”)
name: Optional name for File when using data=
annotations: Optional MCP annotations for the content

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

This automatic behavior enables clients to receive machine-readable data alongside human-readable content without requiring explicit output schemas for object-like returns.

Dictionaries and Objects

When your tool returns a dictionary, dataclass, or Pydantic model, FastMCP automatically creates structured content from it. The structured content contains the actual object data, making it easy for clients to deserialize back to native objects.

@mcp.tool
def get_user_data(user_id: str) -> dict:
    """Get user data."""
    return {"name": "Alice", "age": 30, "active": True}

Primitives and Collections

When your tool returns a primitive type (int, str, bool) or a collection (list, set), FastMCP needs a return type annotation to generate structured content. The annotation tells FastMCP how to validate and serialize the result. Without a type annotation, the tool only produces content:

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

When you add a return annotation, such as -> int, FastMCP generates structuredContent by wrapping the primitive value in a {"result": ...} object, since JSON schemas require object-type roots for structured output:

@mcp.tool
def calculate_sum(a: int, b: int) -> int:
    """Calculate sum with return annotation."""
    return a + b  # Returns 8

Typed Models

Return type annotations work with any type that can be converted to a JSON schema. Dataclasses and Pydantic models are particularly useful because FastMCP extracts their field definitions to create detailed schemas.

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",
    )

The Person dataclass becomes an output schema (second tab) that describes the expected format. When executed, clients receive the result (third tab) with both content and structuredContent fields.

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.

Important Constraints:

Output schemas must be object types ("type": "object")
If you provide an output schema, your tool must return structured output that matches it
However, you can provide structured output without an output schema (using ToolResult)

ToolResult and Metadata

For complete control over tool responses, return a ToolResult object. This gives you explicit control over all aspects of the tool’s output: traditional content, structured data, and metadata.

from fastmcp.tools.tool import ToolResult
from mcp.types import TextContent

@mcp.tool
def advanced_tool() -> ToolResult:
    """Tool with full control over output."""
    return ToolResult(
        content=[TextContent(type="text", text="Human-readable summary")],
        structured_content={"data": "value", "count": 42},
        meta={"execution_time_ms": 145}
    )

ToolResult accepts three fields: content - The traditional MCP content blocks that clients display to users. Can be a string (automatically converted to TextContent), a list of MCP content blocks, or any serializable value (converted to JSON string). At least one of content or structured_content must be provided.

# Simple string
ToolResult(content="Hello, world!")

# List of content blocks
ToolResult(content=[
    TextContent(type="text", text="Result: 42"),
    ImageContent(type="image", data="base64...", mimeType="image/png")
])

structured_content - A dictionary containing structured data that matches your tool’s output schema. This enables clients to programmatically process the results. If you provide structured_content, it must be a dictionary or None. If only structured_content is provided, it will also be used as content (converted to JSON string).

ToolResult(
    content="Found 3 users",
    structured_content={"users": [{"name": "Alice"}, {"name": "Bob"}]}
)

meta Runtime metadata about the tool execution. Use this for performance metrics, debugging information, or any client-specific data that doesn’t belong in the content or structured output.

ToolResult(
    content="Analysis complete",
    structured_content={"result": "positive"},
    meta={
        "execution_time_ms": 145,
        "model_version": "2.1",
        "confidence": 0.95
    }
)

The meta field in ToolResult is for runtime metadata about tool execution (e.g., execution time, performance metrics). This is separate from the meta parameter in @mcp.tool(meta={...}), which provides static metadata about the tool definition itself.

When returning ToolResult, you have full control - FastMCP won’t automatically wrap or transform your data. ToolResult can be returned with or without an output schema.

Custom Serialization

When you need custom serialization (like YAML, Markdown tables, or specialized formats), return ToolResult with your serialized content. This makes the serialization explicit and visible in your tool’s code:

import yaml
from fastmcp import FastMCP
from fastmcp.tools.tool import ToolResult

mcp = FastMCP("MyServer")

@mcp.tool
def get_config() -> ToolResult:
    """Returns configuration as YAML."""
    data = {"api_key": "abc123", "debug": True, "rate_limit": 100}
    return ToolResult(
        content=yaml.dump(data, sort_keys=False),
        structured_content=data
    )

For reusable serialization across multiple tools, create a wrapper decorator that returns ToolResult. This lets you compose serializers with other behaviors (logging, validation, caching) and keeps the serialization visible at the tool definition. See examples/custom_tool_serializer_decorator.py for a complete implementation.

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:

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

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

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.

Timeouts

Tools can specify a timeout parameter to limit how long execution can take. When the timeout is exceeded, the client receives an MCP error and the tool stops processing. This protects your server from unexpectedly slow operations that could block resources or leave clients waiting indefinitely.

from fastmcp import FastMCP

mcp = FastMCP()

@mcp.tool(timeout=30.0)
async def fetch_data(url: str) -> dict:
    """Fetch data with a 30-second timeout."""
    # If this takes longer than 30 seconds,
    # the client receives an MCP error
    ...

Timeouts are specified in seconds as a float. When a tool exceeds its timeout, FastMCP returns an MCP error with code -32000 and a message indicating which tool timed out and how long it ran. Both sync and async tools support timeouts—sync functions run in thread pools, so the timeout applies to the entire operation regardless of execution model.

Tools must explicitly opt-in to timeouts. There is no server-level default timeout setting.

Timeouts vs Background Tasks

Timeouts apply to foreground execution—when a tool runs directly in response to a client request. They protect your server from tools that unexpectedly hang due to network issues, resource contention, or other transient problems.

The timeout parameter does not apply to background tasks. When a tool runs as a background task (task=True), execution happens in a Docket worker where the FastMCP timeout is not enforced.For task timeouts, use Docket’s Timeout dependency directly in your function signature:

from datetime import timedelta
from docket import Timeout

@mcp.tool(task=True)
async def long_running_task(
    data: str,
    timeout: Timeout = Timeout(timedelta(minutes=10))
) -> str:
    """Task with a 10-minute timeout enforced by Docket."""
    ...

See the Docket documentation for more on task timeouts and retries.

When a tool times out, FastMCP logs a warning suggesting task mode. For operations you know will be long-running, use task=True instead—background tasks offload work to distributed workers and let clients poll for progress.

Component Visibility

You can control which tools are enabled for clients using server-level enabled control. Disabled tools don’t appear in list_tools and can’t be called.

from fastmcp import FastMCP

mcp = FastMCP("MyServer")

@mcp.tool(tags={"admin"})
def admin_action() -> str:
    """Admin-only action."""
    return "Done"

@mcp.tool(tags={"public"})
def public_action() -> str:
    """Public action."""
    return "Done"

# Disable specific tools by key
mcp.disable(keys={"tool:admin_action"})

# Disable tools by tag
mcp.disable(tags={"admin"})

# Or use allowlist mode - only enable tools with specific tags
mcp.enable(tags={"public"}, only=True)

See Visibility for the complete visibility control API including key formats, tag-based filtering, and provider-level control.

MCP 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. FastMCP accepts either a plain dict or ToolAnnotations; the examples below use ToolAnnotations for consistency and stronger editor/type support.

from mcp.types import ToolAnnotations

@mcp.tool(
    annotations=ToolAnnotations(
        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:

Annotation	Type	Default	Purpose
`title`	string	-	Display name for user interfaces
`readOnlyHint`	boolean	false	Indicates if the tool only reads without making changes
`destructiveHint`	boolean	true	For non-readonly tools, signals if changes are destructive
`idempotentHint`	boolean	false	Indicates if repeated identical calls have the same effect as a single call
`openWorldHint`	boolean	true	Specifies if the tool interacts with external systems

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.

Using Annotation Hints

MCP clients like Claude and ChatGPT use annotation hints to determine when to skip confirmation prompts and how to present tools to users. The most commonly used hint is readOnlyHint, which signals that a tool only reads data without making changes. Read-only tools improve user experience by:

Skipping confirmation prompts for safe operations
Allowing broader access without security concerns
Enabling more aggressive batching and caching

Mark a tool as read-only when it retrieves data, performs calculations, or checks status without modifying state:

from fastmcp import FastMCP
from mcp.types import ToolAnnotations

mcp = FastMCP("Data Server")

@mcp.tool(annotations=ToolAnnotations(readOnlyHint=True))
def get_user(user_id: str) -> dict:
    """Retrieve user information by ID."""
    return {"id": user_id, "name": "Alice"}

@mcp.tool(
    annotations=ToolAnnotations(
        readOnlyHint=True,
        idempotentHint=True,  # Same result for repeated calls
        openWorldHint=False   # Only internal data
    )
)
def search_products(query: str) -> list[dict]:
    """Search the product catalog."""
    return [{"id": 1, "name": "Widget", "price": 29.99}]

# Write operations - no readOnlyHint
@mcp.tool()
def update_user(user_id: str, name: str) -> dict:
    """Update user information."""
    return {"id": user_id, "name": name, "updated": True}

@mcp.tool(annotations=ToolAnnotations(destructiveHint=True))
def delete_user(user_id: str) -> dict:
    """Permanently delete a user account."""
    return {"deleted": user_id}

For tools that write to databases, send notifications, create/update/delete resources, or trigger workflows, omit readOnlyHint or set it to False. Use destructiveHint=True for operations that cannot be undone. Client-specific behavior:

ChatGPT: Skips confirmation prompts for read-only tools in Chat mode (see ChatGPT integration)
Claude: Uses hints to understand tool safety profiles and make better execution decisions

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
mcp.disable(keys={"tool:example_tool"}) # Sends tools/list_changed notification
mcp.enable(keys={"tool:example_tool"})  # Sends tools/list_changed notification
mcp.local_provider.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.

Accessing the 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.

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 through its local provider:

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.local_provider.remove_tool("calculate_sum")

Versioning

Tools support versioning, allowing you to maintain multiple implementations under the same name while clients automatically receive the highest version. See Versioning for complete documentation on version comparison, retrieval, and migration patterns.

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Documentation Index

​The @tool Decorator

​Decorator Arguments

@tool Decorator Arguments

​Using with Methods

​Async Support

​Thread affinity

​Arguments

​Type Annotations

​Optional Arguments

​Validation Modes

​Parameter Metadata

​Docstring Descriptions

​Simple String Descriptions

​Advanced Metadata with Field

​Hiding Parameters from the LLM

​Return Values

​Content Blocks

​Media Helper Classes

​Structured Output

​Dictionaries and Objects

​Primitives and Collections

​Typed Models

​Output Schemas

​Primitive Type Wrapping

​Manual Schema Control

​ToolResult and Metadata

​Custom Serialization

​Error Handling

​Timeouts

​Timeouts vs Background Tasks

​Component Visibility

​MCP Annotations

​Using Annotation Hints

​Notifications

​Accessing the MCP Context

​Server Behavior

​Duplicate Tools

​Removing Tools

​Versioning

The `@tool` Decorator

Decorator Arguments

Using with Methods

Async Support

Thread affinity

Arguments

Type Annotations

Optional Arguments

Validation Modes

Parameter Metadata

Docstring Descriptions

Simple String Descriptions

Advanced Metadata with Field

Hiding Parameters from the LLM

Return Values

Content Blocks

Media Helper Classes

Structured Output

Dictionaries and Objects

Primitives and Collections

Typed Models

Output Schemas

Primitive Type Wrapping

Manual Schema Control

ToolResult and Metadata

Custom Serialization

Error Handling

Timeouts

Timeouts vs Background Tasks

Component Visibility

MCP Annotations

Using Annotation Hints

Notifications

Accessing the MCP Context

Server Behavior

Duplicate Tools

Removing Tools

Versioning