arcade-mcp/libs/arcade-mcp-server/docs/examples/01_tools.md

01 - Tools

Learn how to create tools with different parameter types and how arcade_mcp_server discovers them automatically.

Running the Example

• Run (HTTP default): uv run 01_tools.py
• Run (stdio for Claude Desktop): uv run 01_tools.py stdio

Source Code

--8<-- "docs/examples/01_tools.py"

Creating Tools

1. Simple Tools

Basic tools with simple parameter types:

@app.tool
def hello(name: Annotated[str, "Name to greet"]) -> str:
    """Say hello to someone."""
    return f"Hello, {name}!"

@app.tool
def add(
    a: Annotated[float, "First number"],
    b: Annotated[float, "Second number"]
) -> Annotated[float, "Sum of the numbers"]:
    """Add two numbers together."""
    return a + b

2. List Parameters

Working with lists of values:

@app.tool
def calculate_average(
    numbers: Annotated[list[float], "List of numbers to average"]
) -> Annotated[float, "Average of all numbers"]:
    """Calculate the average of a list of numbers."""
    if not numbers:
        return 0.0
    return sum(numbers) / len(numbers)

3. Complex Types with TypedDict

Using TypedDict for structured input and output:

class PersonInfo(TypedDict):
    name: str
    age: int
    email: str
    is_active: bool

@tool
def create_user_profile(
    person: Annotated[PersonInfo, "Person's information"]
) -> Annotated[str, "Formatted user profile"]:
    """Create a formatted user profile from person information."""
    # Implementation here

Managing Tools in MCPApp

With the direct Python approach, you have full control over your tools:

1. Defining Tools Directily

Use @app.tool to define tools directly on your MCPApp instance:

@app.tool
def my_tool(param: str) -> str:
    """Tool description."""
    return f"Processed: {param}"

2. Importing Tools from Files

You can import tools from other files and add them explicitly:

from my_tools import calculate, process_data

# Add imported tools to the app
app.add_tool(calculate)
app.add_tool(process_data)

3. Project Organization

Example project structure:

my_project/
├── server.py          # Main MCPApp
├── tools/
│   ├── math.py       # Tools using @tool decorator
│   └── utils.py      # More tools
└── pyproject.toml    # Dependencies

This approach gives you explicit control over which tools are loaded and how they're organized.

Best Practices

Parameter Annotations

• Always use Annotated: Provide descriptions for all parameters
• Clear descriptions: Help the AI understand what each parameter does
• Type hints: Use proper Python type hints for validation

Tool Design

• Single purpose: Each tool should do one thing well
• Error handling: Add validation and helpful error messages
• Return types: Always annotate return types with descriptions

Organization

• Group related tools: Use directories to organize by functionality
• Naming conventions: Use clear, descriptive names
• Documentation: Write clear docstrings for each tool

Key Concepts

• Explicit Control: Use @app.tool decorators and app.add_tool() for precise tool management
• Type Safety: Full type annotation support with runtime validation
• TypedDict Support: Use TypedDict for complex structured data
• Import Flexibility: Import tools from your own files and external packages
• Direct Execution: Run servers directly with uv run for better development experience