New in version: 2.8.0 Tool transformation allows you to create new, enhanced tools from existing ones. This powerful feature enables you to adapt tools for different contexts, simplify complex interfaces, or add custom logic without duplicating code.

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Why Transform Tools?

Often, an existing tool is almost perfect for your use case, but it might have:
  • A confusing description (or no description at all).
  • Argument names or descriptions that are not intuitive for an LLM (e.g., q instead of query).
  • Unnecessary parameters that you want to hide from the LLM.
  • A need for input validation before the original tool is called.
  • A need to modify or format the tool’s output.
Instead of rewriting the tool from scratch, you can transform it to fit your needs.

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Basic Transformation

The primary way to create a transformed tool is with the Tool.from_tool() class method. At its simplest, you can use it to change a tool’s top-level metadata like its name, description, or tags. In the following simple example, we take a generic search tool and adjust its name and description to help an LLM client better understand its purpose. 
from fastmcp import FastMCP
from fastmcp.tools import Tool

mcp = FastMCP()

# The original, generic tool
@mcp.tool
def search(query: str, category: str = "all") -> list[dict]:
    """Searches for items in the database."""
    return database.search(query, category)

# Create a more domain-specific version by changing its metadata
product_search_tool = Tool.from_tool(
    search,
    name="find_products",
    description="""
        Search for products in the e-commerce catalog. 
        Use this when customers ask about finding specific items, 
        checking availability, or browsing product categories.
        """,
)

mcp.add_tool(product_search_tool)
When you transform a tool, the original tool remains registered on the server. To avoid confusing an LLM with two similar tools, you can disable the original one:
from fastmcp import FastMCP
from fastmcp.tools import Tool

mcp = FastMCP()

# The original, generic tool
@mcp.tool
def search(query: str, category: str = "all") -> list[dict]:
    ...

# Create a more domain-specific version
product_search_tool = Tool.from_tool(search, ...)
mcp.add_tool(product_search_tool)

# Disable the original tool
search.disable()
Now, clients see a tool named find_products with a clear, domain-specific purpose and relevant tags, even though it still uses the original generic search function’s logic.

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Parameters

The Tool.from_tool() class method is the primary way to create a transformed tool. It takes the following parameters:
  • tool: The tool to transform. This is the only required argument.
  • name: An optional name for the new tool.
  • description: An optional description for the new tool.
  • transform_args: A dictionary of ArgTransform objects, one for each argument you want to modify.
  • transform_fn: An optional function that will be called instead of the parent tool’s logic.
  • output_schema: Control output schema and structured outputs (see Output Schema Control).
  • tags: An optional set of tags for the new tool.
  • annotations: An optional set of ToolAnnotations for the new tool.
  • serializer: An optional function that will be called to serialize the result of the new tool.
  • meta: Control meta information for the tool. Use None to remove meta, any dict to set meta, or leave unset to inherit from parent.
The result is a new TransformedTool object that wraps the parent tool and applies the transformations you specify. You can add this tool to your MCP server using its add_tool() method.

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Modifying Arguments

To modify a tool’s parameters, provide a dictionary of ArgTransform objects to the transform_args parameter of Tool.from_tool(). Each key is the name of the original argument you want to modify.
You only need to provide a transform_args entry for arguments you want to modify. All other arguments will be passed through unchanged.

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The ArgTransform Class

To modify an argument, you need to create an ArgTransform object. This object has the following parameters:
  • name: The new name for the argument.
  • description: The new description for the argument.
  • default: The new default value for the argument.
  • default_factory: A function that will be called to generate a default value for the argument. This is useful for arguments that need to be generated for each tool call, such as timestamps or unique IDs.
  • hide: Whether to hide the argument from the LLM.
  • required: Whether the argument is required, usually used to make an optional argument be required instead.
  • type: The new type for the argument.
Certain combinations of parameters are not allowed. For example, you can only use default_factory with hide=True, because dynamic defaults cannot be represented in a JSON schema for the client. You can only set required=True for arguments that do not declare a default value.

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Descriptions

By far the most common reason to transform a tool, after its own description, is to improve its argument descriptions. A good description is crucial for helping an LLM understand how to use a parameter correctly. This is especially important when wrapping tools from external APIs, whose argument descriptions may be missing or written for developers, not LLMs. In this example, we add a helpful description to the user_id argument: 
from fastmcp import FastMCP
from fastmcp.tools import Tool
from fastmcp.tools.tool_transform import ArgTransform

mcp = FastMCP()

@mcp.tool
def find_user(user_id: str):
    """Finds a user by their ID."""
    ...

new_tool = Tool.from_tool(
    find_user,
    transform_args={
        "user_id": ArgTransform(
            description=(
                "The unique identifier for the user, "
                "usually in the format 'usr-xxxxxxxx'."
            )
        )
    }
)

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Names

At times, you may want to rename an argument to make it more intuitive for an LLM. For example, in the following example, we take a generic q argument and expand it to search_query: 
from fastmcp import FastMCP
from fastmcp.tools import Tool
from fastmcp.tools.tool_transform import ArgTransform

mcp = FastMCP()

@mcp.tool
def search(q: str):
    """Searches for items in the database."""
    return database.search(q)

new_tool = Tool.from_tool(
    search,
    transform_args={
        "q": ArgTransform(name="search_query")
    }
)

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Default Values

You can update the default value for any argument using the default parameter. Here, we change the default value of the y argument to 10: 
from fastmcp import FastMCP
from fastmcp.tools import Tool
from fastmcp.tools.tool_transform import ArgTransform

mcp = FastMCP()

@mcp.tool
def add(x: int, y: int) -> int:
    """Adds two numbers."""
    return x + y

new_tool = Tool.from_tool(
    add,
    transform_args={
        "y": ArgTransform(default=10)
    }
)
Default values are especially useful in combination with hidden arguments.

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Hiding Arguments

Sometimes a tool requires arguments that shouldn’t be exposed to the LLM, such as API keys, configuration flags, or internal IDs. You can hide these parameters using hide=True. Note that you can only hide arguments that have a default value (or for which you provide a new default), because the LLM can’t provide a value at call time.
To pass a constant value to the parent tool, combine hide=True with default=<value>.
import os
from fastmcp import FastMCP
from fastmcp.tools import Tool
from fastmcp.tools.tool_transform import ArgTransform

mcp = FastMCP()

@mcp.tool
def send_email(to: str, subject: str, body: str, api_key: str):
    """Sends an email."""
    ...
    
# Create a simplified version that hides the API key
new_tool = Tool.from_tool(
    send_email,
    name="send_notification",
    transform_args={
        "api_key": ArgTransform(
            hide=True, 
            default=os.environ.get("EMAIL_API_KEY"),
        )
    }
)
The LLM now only sees the to, subject, and body parameters. The api_key is supplied automatically from an environment variable. For values that must be generated for each tool call (like timestamps or unique IDs), use default_factory, which is called with no arguments every time the tool is called. For example, 
transform_args = {
    'timestamp': ArgTransform(
        hide=True,
        default_factory=lambda: datetime.now(),
    )
}
default_factory can only be used with hide=True. This is because visible parameters need static defaults that can be represented in a JSON schema for the client.

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Meta Information

New in version: 2.11.0 You can control meta information on transformed tools using the meta parameter. Meta information is additional data about the tool that doesn’t affect its functionality but can be used by clients for categorization, routing, or other purposes. 
from fastmcp import FastMCP
from fastmcp.tools import Tool

mcp = FastMCP()

@mcp.tool
def analyze_data(data: str) -> dict:
    """Analyzes the provided data."""
    return {"result": f"Analysis of {data}"}

# Add custom meta information
enhanced_tool = Tool.from_tool(
    analyze_data,
    name="enhanced_analyzer",
    meta={
        "category": "analytics",
        "priority": "high",
        "requires_auth": True
    }
)

mcp.add_tool(enhanced_tool)
You can also remove meta information entirely: 
# Remove meta information from parent tool
simplified_tool = Tool.from_tool(
    analyze_data,
    name="simple_analyzer", 
    meta=None  # Removes any meta information
)
If you don’t specify the meta parameter, the transformed tool inherits the parent tool’s meta information.

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Required Values

In rare cases where you want to make an optional argument required, you can set required=True. This has no effect if the argument was already required. 
transform_args = {
    'user_id': ArgTransform(
        required=True,
    )
}

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Modifying Tool Behavior

With great power comes great responsibility. Modifying tool behavior is a very advanced feature.
In addition to changing a tool’s schema, advanced users can also modify its behavior. This is useful for adding validation logic, or for post-processing the tool’s output. The from_tool() method takes a transform_fn parameter, which is an async function that replaces the parent tool’s logic and gives you complete control over the tool’s execution.

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The Transform Function

The transform_fn is an async function that completely replaces the parent tool’s logic. Critically, the transform function’s arguments are used to determine the new tool’s final schema. Any arguments that are not already present in the parent tool schema OR the transform_args will be added to the new tool’s schema. Note that when transform_args and your function have the same argument name, the transform_args metadata will take precedence, if provided. 
async def my_custom_logic(user_input: str, max_length: int = 100) -> str:
    # Your custom logic here - this completely replaces the parent tool
    return f"Custom result for: {user_input[:max_length]}"

Tool.from_tool(transform_fn=my_custom_logic)
The name / docstring of the transform_fn are ignored. Only its arguments are used to determine the final schema.

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Calling the Parent Tool

Most of the time, you don’t want to completely replace the parent tool’s behavior. Instead, you want to add validation, modify inputs, or post-process outputs while still leveraging the parent tool’s core functionality. For this, FastMCP provides the special forward() and forward_raw() functions. Both forward() and forward_raw() are async functions that let you call the parent tool from within your transform_fn:
  • forward() (recommended): Automatically handles argument mapping based on your ArgTransform configurations. Call it with the transformed argument names.
  • forward_raw(): Bypasses all transformation and calls the parent tool directly with its original argument names. This is rarely needed unless you’re doing complex argument manipulation, perhaps without arg_transforms.
The most common transformation pattern is to validate (potentially renamed) arguments before calling the parent tool. Here’s an example that validates that x and y are positive before calling the parent tool:
In the simplest case, your parent tool and your transform function have the same arguments. You can call forward() with the same argument names as the parent tool:
from fastmcp import FastMCP
from fastmcp.tools import Tool
from fastmcp.tools.tool_transform import forward

mcp = FastMCP()

@mcp.tool
def add(x: int, y: int) -> int:
    """Adds two numbers."""
    return x + y

async def ensure_positive(x: int, y: int) -> int:
    if x <= 0 or y <= 0:
        raise ValueError("x and y must be positive")
    return await forward(x=x, y=y)

new_tool = Tool.from_tool(
    add,
    transform_fn=ensure_positive,
)

mcp.add_tool(new_tool)

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Passing Arguments with **kwargs

If your transform_fn includes **kwargs in its signature, it will receive all arguments from the parent tool after ArgTransform configurations have been applied. This is powerful for creating flexible validation functions that don’t require you to add every argument to the function signature. In the following example, we wrap a parent tool that accepts two arguments x and y. These are renamed to a and b in the transformed tool, and the transform only validates a, passing the other argument through as **kwargs. 
from fastmcp import FastMCP
from fastmcp.tools import Tool
from fastmcp.tools.tool_transform import forward

mcp = FastMCP()

@mcp.tool
def add(x: int, y: int) -> int:
    """Adds two numbers."""
    return x + y

async def ensure_a_positive(a: int, **kwargs) -> int:
    if a <= 0:
        raise ValueError("a must be positive")
    return await forward(a=a, **kwargs)

new_tool = Tool.from_tool(
    add,
    transform_fn=ensure_a_positive,
    transform_args={
        "x": ArgTransform(name="a"),
        "y": ArgTransform(name="b"),
    }
)

mcp.add_tool(new_tool)
In the above example, **kwargs receives the renamed argument b, not the original argument y. It is therefore recommended to use with forward(), not forward_raw().

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Modifying MCP Tools with MCPConfig

When running MCP Servers under FastMCP with MCPConfig, you can also apply a subset of tool transformations directly in the MCPConfig json file. 
{
    "mcpServers": {
        "weather": {
            "url": "https://weather.example.com/mcp",
            "transport": "http",
            "tools": {
                "weather_get_forecast": {
                    "name": "miami_weather",
                    "description": "Get the weather for Miami",
                    "meta": {
                        "category": "weather",
                        "location": "miami"
                    },
                    "arguments": {
                        "city": {
                            "name": "city",
                            "default": "Miami",
                            "hide": True,
                        }
                    }
                }
            }
        }
    }
}
The tools section is a dictionary of tool names to tool configurations. Each tool configuration is a dictionary of tool properties. See the MCPConfigTransport documentation for more details.

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Output Schema Control

New in version: 2.10.0 Transformed tools inherit output schemas from their parent by default, but you can control this behavior: Inherit from Parent (Default) 
Tool.from_tool(parent_tool, name="renamed_tool")
The transformed tool automatically uses the parent tool’s output schema and structured output behavior. Custom Output Schema 
Tool.from_tool(parent_tool, output_schema={
    "type": "object", 
    "properties": {"status": {"type": "string"}}
})
Provide your own schema that differs from the parent. The tool must return data matching this schema. Remove Output Schema 
Tool.from_tool(parent_tool, output_schema=False)
Removes the output schema declaration. Automatic structured content still works for object-like returns (dict, dataclass, Pydantic models) but primitive types won’t be structured. Full Control with Transform Functions 
async def custom_output(**kwargs) -> ToolResult:
    result = await forward(**kwargs)
    return ToolResult(content=[...], structured_content={...})

Tool.from_tool(parent_tool, transform_fn=custom_output)
Use a transform function returning ToolResult for complete control over both content blocks and structured outputs.

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Common Patterns

Tool transformation is a flexible feature that supports many powerful patterns. Here are a few common use cases to give you ideas.

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Adapting Remote or Generated Tools

This is one of the most common reasons to use tool transformation. Tools from remote servers (via a proxy) or generated from an OpenAPI spec are often too generic for direct use by an LLM. You can use transformation to create a simpler, more intuitive version for your specific needs.

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Chaining Transformations

You can chain transformations by using an already transformed tool as the parent for a new transformation. This lets you build up complex behaviors in layers, for example, first renaming arguments, and then adding validation logic to the renamed tool.

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Context-Aware Tool Factories

You can write functions that act as “factories,” generating specialized versions of a tool for different contexts. For example, you could create a get_my_data tool that is specific to the currently logged-in user by hiding the user_id parameter and providing it automatically.