add-tool

Add Tool Command

Add a new tool function to an existing LangGraph agent file.

Workflow

1. Parse arguments:

   • First argument: tool name (required)
   • Second argument: file path (optional, will search for agent files)

2. If no file specified, search for likely agent files:

   • agent.py, graph.py, main.py
   • Files containing StateGraph or create_agent

3. Read the target file to understand its structure

4. Ask user what the tool should do (unless obvious from name)

5. Generate tool code following this pattern:

@tool
def tool_name(param1: str, param2: int = 10) -> str:
    """Brief description of what the tool does.

    Args:
        param1: Description of param1
        param2: Description of param2 (default: 10)
    """
    # Implementation
    return result

1. Insert the tool in the appropriate location:

   • After existing tool definitions
   • Before the model/graph setup

2. Update tool registration:

   • For create_agent: Add to tools=[...] list
   • For StateGraph: Add to tools = [...] and tools_by_name = {...}

Tool Template

@tool
def {tool_name}({parameters}) -> {return_type}:
    """{description}

    Args:
        {arg_docs}
    """
    {implementation}
    return {result}

Best Practices to Apply

1. Always include docstring with Args section - LLMs use this
2. Use type hints for all parameters and return value
3. Keep tools focused - one clear purpose
4. Return strings for simple tools (easier for LLM to process)
5. Handle errors gracefully - return error messages, don't raise

Example Additions

API Tool

@tool
def fetch_weather(city: str) -> str:
    """Get current weather for a city.

    Args:
        city: City name (e.g., "San Francisco")
    """
    # TODO: Call weather API
    return f"Weather in {city}: Sunny, 72°F"

Database Tool

@tool
def query_database(sql: str) -> str:
    """Execute a read-only SQL query.

    Args:
        sql: SQL SELECT query to execute
    """
    # TODO: Implement with proper connection handling
    return "Query results..."