Tool Transformation

Modify tool schemas - rename, reshape arguments, and customize behavior

New in version 3.0.0

Tool transformation lets you modify tool schemas - renaming tools, changing descriptions, adjusting tags, and reshaping argument schemas. FastMCP provides two mechanisms that share the same configuration options but differ in timing.

Deferred transformation with ToolTransform applies modifications when tools flow through a transform chain. Use this for tools from mounted servers, proxies, or other providers where you don’t control the source directly.

Immediate transformation with Tool.from_tool() creates a modified tool object right away. Use this when you have direct access to a tool and want to transform it before registration.

ToolTransform

The ToolTransform class is a transform that modifies tools as they flow through a provider. Provide a dictionary mapping original tool names to their transformation configuration.

from fastmcp import FastMCP
from fastmcp.server.transforms import ToolTransform
from fastmcp.tools.tool_transform import ToolTransformConfig

mcp = FastMCP("Server")

@mcp.tool
def verbose_internal_data_fetcher(query: str) -> str:
    """Fetches data from the internal database."""
    return f"Results for: {query}"

# Rename the tool to something simpler
mcp.add_transform(ToolTransform({
    "verbose_internal_data_fetcher": ToolTransformConfig(
        name="search",
        description="Search the database.",
    )
}))

# Clients see "search" with the cleaner description

ToolTransform is useful when you want to modify tools from mounted or proxied servers without changing the original source.

Tool.from_tool()

Use Tool.from_tool() when you have the tool object and want to create a transformed version for registration.

from fastmcp import FastMCP
from fastmcp.tools import Tool, tool
from fastmcp.tools.tool_transform import ArgTransform

# Create a tool without registering it
@tool
def search(q: str, limit: int = 10) -> list[str]:
    """Search for items."""
    return [f"Result {i} for {q}" for i in range(limit)]

# Transform it before registration
better_search = Tool.from_tool(
    search,
    name="find_items",
    description="Find items matching your search query.",
    transform_args={
        "q": ArgTransform(
            name="query",
            description="The search terms to look for.",
        ),
    },
)

mcp = FastMCP("Server")
mcp.add_tool(better_search)

The standalone @tool decorator (from fastmcp.tools) creates a Tool object without registering it to any server. This separates creation from registration, letting you transform tools before deciding where they go.

Modification Options

Both mechanisms support the same modifications.

Tool-level options:

Option	Description
name	New name for the tool
description	New description
title	Human-readable title
tags	Set of tags for categorization
annotations	MCP ToolAnnotations
meta	Custom metadata dictionary
enabled	Whether the tool is visible to clients (default True)

Argument-level options (via ArgTransform or ArgTransformConfig):

Option	Description
name	Rename the argument
description	New description for the argument
default	New default value
default_factory	Callable that generates a default (requires hide=True)
hide	Remove from client-visible schema
required	Make an optional argument required
type	Change the argument’s type
examples	Example values for the argument

Hiding Arguments

Hide arguments to simplify the interface or inject values the client shouldn’t control.

from fastmcp.tools.tool_transform import ArgTransform

# Hide with a constant value
transform_args = {
    "api_key": ArgTransform(hide=True, default="secret-key"),
}

# Hide with a dynamic value
import uuid
transform_args = {
    "request_id": ArgTransform(hide=True, default_factory=lambda: str(uuid.uuid4())),
}

Hidden arguments disappear from the tool’s schema. The client never sees them, but the underlying function receives the configured value.

default_factory requires hide=True. Visible arguments need static defaults that can be represented in JSON Schema.

Renaming Arguments

Rename arguments to make them more intuitive for LLMs or match your API conventions.

from fastmcp.tools import Tool, tool
from fastmcp.tools.tool_transform import ArgTransform

@tool
def search(q: str, n: int = 10) -> list[str]:
    """Search for items."""
    return []

better_search = Tool.from_tool(
    search,
    transform_args={
        "q": ArgTransform(name="query", description="Search terms"),
        "n": ArgTransform(name="max_results", description="Maximum results to return"),
    },
)

Custom Transform Functions

For advanced scenarios, provide a transform_fn that intercepts tool execution. The function can validate inputs, modify outputs, or add custom logic while still calling the original tool via forward().

from fastmcp import FastMCP
from fastmcp.tools import Tool, tool
from fastmcp.tools.tool_transform import forward, ArgTransform

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

async def safe_divide(numerator: float, denominator: float) -> float:
    if denominator == 0:
        raise ValueError("Cannot divide by zero")
    return await forward(numerator=numerator, denominator=denominator)

safe_division = Tool.from_tool(
    divide,
    name="safe_divide",
    transform_fn=safe_divide,
    transform_args={
        "a": ArgTransform(name="numerator"),
        "b": ArgTransform(name="denominator"),
    },
)

mcp = FastMCP("Server")
mcp.add_tool(safe_division)

The forward() function handles argument mapping automatically. Call it with the transformed argument names, and it maps them back to the original function’s parameters.

For direct access to the original function without mapping, use forward_raw() with the original parameter names.