New in version: 2.0.0 Tools are executable functions exposed by MCP servers. The FastMCP client provides methods to discover available tools and execute them with arguments.

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Discovering Tools

Use list_tools() to retrieve all tools available on the server: 
async with client:
    tools = await client.list_tools()
    # tools -> list[mcp.types.Tool]
    
    for tool in tools:
        print(f"Tool: {tool.name}")
        print(f"Description: {tool.description}")
        if tool.inputSchema:
            print(f"Parameters: {tool.inputSchema}")
        # Access tags and other metadata
        if hasattr(tool, '_meta') and tool._meta:
            fastmcp_meta = tool._meta.get('_fastmcp', {})
            print(f"Tags: {fastmcp_meta.get('tags', [])}")

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Filtering by Tags

New in version: 2.11.0 You can use the meta field to filter tools based on their tags: 
async with client:
    tools = await client.list_tools()
    
    # Filter tools by tag
    analysis_tools = [
        tool for tool in tools 
        if hasattr(tool, '_meta') and tool._meta and
           tool._meta.get('_fastmcp', {}) and
           'analysis' in tool._meta.get('_fastmcp', {}).get('tags', [])
    ]
    
    print(f"Found {len(analysis_tools)} analysis tools")
The _meta field is part of the standard MCP specification. FastMCP servers include tags and other metadata within a _fastmcp namespace (e.g., _meta._fastmcp.tags) to avoid conflicts with user-defined metadata. This behavior can be controlled with the server’s include_fastmcp_meta setting - when disabled, the _fastmcp namespace won’t be included. Other MCP server implementations may not provide this metadata structure.

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Executing Tools

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Basic Execution

Execute a tool using call_tool() with the tool name and arguments: 
async with client:
    # Simple tool call
    result = await client.call_tool("add", {"a": 5, "b": 3})
    # result -> CallToolResult with structured and unstructured data
    
    # Access structured data (automatically deserialized)
    print(result.data)  # 8 (int) or {"result": 8} for primitive types
    
    # Access traditional content blocks  
    print(result.content[0].text)  # "8" (TextContent)

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Advanced Execution Options

The call_tool() method supports additional parameters for timeout control and progress monitoring: 
async with client:
    # With timeout (aborts if execution takes longer than 2 seconds)
    result = await client.call_tool(
        "long_running_task", 
        {"param": "value"}, 
        timeout=2.0
    )
    
    # With progress handler (to track execution progress)
    result = await client.call_tool(
        "long_running_task",
        {"param": "value"},
        progress_handler=my_progress_handler
    )
Parameters:
  • name: The tool name (string)
  • arguments: Dictionary of arguments to pass to the tool (optional)
  • timeout: Maximum execution time in seconds (optional, overrides client-level timeout)
  • progress_handler: Progress callback function (optional, overrides client-level handler)

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Handling Results

New in version: 2.10.0 Tool execution returns a CallToolResult object with both structured and traditional content. FastMCP’s standout feature is the .data property, which doesn’t just provide raw JSON but actually hydrates complete Python objects including complex types like datetimes, UUIDs, and custom classes.

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CallToolResult Properties

CallToolResult Properties

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.data
Any
FastMCP exclusive: Fully hydrated Python objects with complex type support (datetimes, UUIDs, custom classes). Goes beyond JSON to provide complete object reconstruction from output schemas.
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.content
list[mcp.types.ContentBlock]
Standard MCP content blocks (TextContent, ImageContent, AudioContent, etc.) available from all MCP servers.
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.structured_content
dict[str, Any] | None
Standard MCP structured JSON data as sent by the server, available from all MCP servers that support structured outputs.
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.is_error
bool
Boolean indicating if the tool execution failed.

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Structured Data Access

FastMCP’s .data property provides fully hydrated Python objects, not just JSON dictionaries. This includes complex type reconstruction: 
from datetime import datetime
from uuid import UUID

async with client:
    result = await client.call_tool("get_weather", {"city": "London"})
    
    # FastMCP reconstructs complete Python objects from the server's output schema
    weather = result.data  # Server-defined WeatherReport object
    print(f"Temperature: {weather.temperature}°C at {weather.timestamp}")
    print(f"Station: {weather.station_id}")
    print(f"Humidity: {weather.humidity}%")
    
    # The timestamp is a real datetime object, not a string!
    assert isinstance(weather.timestamp, datetime)
    assert isinstance(weather.station_id, UUID)
    
    # Compare with raw structured JSON (standard MCP)
    print(f"Raw JSON: {result.structured_content}")
    # {"temperature": 20, "timestamp": "2024-01-15T14:30:00Z", "station_id": "123e4567-..."}
    
    # Traditional content blocks (standard MCP)  
    print(f"Text content: {result.content[0].text}")

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Fallback Behavior

For tools without output schemas or when deserialization fails, .data will be None: 
async with client:
    result = await client.call_tool("legacy_tool", {"param": "value"})
    
    if result.data is not None:
        # Structured output available and successfully deserialized
        print(f"Structured: {result.data}")
    else:
        # No structured output or deserialization failed - use content blocks
        for content in result.content:
            if hasattr(content, 'text'):
                print(f"Text result: {content.text}")
            elif hasattr(content, 'data'):
                print(f"Binary data: {len(content.data)} bytes")

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Primitive Type Unwrapping

FastMCP servers automatically wrap non-object results (like int, str, bool) in a {"result": value} structure to create valid structured outputs. FastMCP clients understand this convention and automatically unwrap the value in .data for convenience, so you get the original primitive value instead of a wrapper object.
async with client:
    result = await client.call_tool("calculate_sum", {"a": 5, "b": 3})
    
    # FastMCP client automatically unwraps for convenience
    print(result.data)  # 8 (int) - the original value
    
    # Raw structured content shows the server-side wrapping
    print(result.structured_content)  # {"result": 8}
    
    # Other MCP clients would need to manually access ["result"]
    # value = result.structured_content["result"]  # Not needed with FastMCP!

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Error Handling

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Exception-Based Error Handling

By default, call_tool() raises a ToolError if the tool execution fails: 
from fastmcp.exceptions import ToolError

async with client:
    try:
        result = await client.call_tool("potentially_failing_tool", {"param": "value"})
        print("Tool succeeded:", result.data)
    except ToolError as e:
        print(f"Tool failed: {e}")

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Manual Error Checking

You can disable automatic error raising and manually check the result: 
async with client:
    result = await client.call_tool(
        "potentially_failing_tool", 
        {"param": "value"}, 
        raise_on_error=False
    )
    
    if result.is_error:
        print(f"Tool failed: {result.content[0].text}")
    else:
        print(f"Tool succeeded: {result.data}")

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Raw MCP Protocol Access

For complete control, use call_tool_mcp() which returns the raw MCP protocol object: 
async with client:
    result = await client.call_tool_mcp("potentially_failing_tool", {"param": "value"})
    # result -> mcp.types.CallToolResult
    
    if result.isError:
        print(f"Tool failed: {result.content}")
    else:
        print(f"Tool succeeded: {result.content}")
        # Note: No automatic deserialization with call_tool_mcp()

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Argument Handling

Arguments are passed as a dictionary to the tool: 
async with client:
    # Simple arguments
    result = await client.call_tool("greet", {"name": "World"})
    
    # Complex arguments
    result = await client.call_tool("process_data", {
        "config": {"format": "json", "validate": True},
        "items": [1, 2, 3, 4, 5],
        "metadata": {"source": "api", "version": "1.0"}
    })
For multi-server clients, tool names are automatically prefixed with the server name (e.g., weather_get_forecast for a tool named get_forecast on the weather server).