Tool Composition: Chaining Tools Together

Overview

Individual tools are powerful. Composed tools are transformative.

Tool composition chains outputs from one tool into inputs of another, creating complex workflows from simple pieces.

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The Power of Composition

Simple Tool + Simple Tool + Simple Tool = Complex Workflow

search_web() → parse_results() → summarize() 
  ↓              ↓                 ↓
"AI trends" → 10 articles → "AI is growing in 3 areas..."

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Tool Chains: Sequential Execution

class ToolChain:
    def __init__(self, *tools):
        self.tools = tools

    def execute(self, initial_input):
        result = initial_input
        for tool in self.tools:
            result = tool.call(result)
            if not result['success']:
                return {"error": result['error']}
        return result

# Usage
chain = ToolChain(
    search_tool,
    parse_tool,
    summarize_tool
)

result = chain.execute("Latest AI breakthroughs")
# Chains: search → parse → summarize

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Conditional Chains

class ConditionalChain:
    def execute(self, data):
        # Step 1: Process
        processed = process_tool.call(data)

        # Step 2: Check quality
        if processed['quality'] > 0.8:
            # High quality: summarize directly
            return summarize_tool.call(processed['data'])
        else:
            # Low quality: enhance first
            enhanced = enhance_tool.call(processed['data'])
            return summarize_tool.call(enhanced['data'])

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Parallel Execution

class ParallelChain:
    async def execute(self, data):
        # Run multiple tools in parallel
        results = await asyncio.gather(
            search_web(data),
            search_docs(data),
            search_archives(data)
        )

        # Aggregate results
        combined = aggregate(results)
        return summarize(combined)

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Error Handling in Chains

class RobustChain:
    def execute(self, data):
        for i, tool in enumerate(self.tools):
            try:
                data = tool.call(data)
            except Exception as e:
                # Try fallback
                fallback = self.fallbacks.get(i)
                if fallback:
                    data = fallback.call(data)
                else:
                    return {"error": f"Step {i} failed: {e}"}
        return {"success": True, "data": data}

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Best Practices

• Use type hints to ensure tool compatibility
• Validate at each step
• Handle errors gracefully
• Monitor performance
• Log execution traces

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Last Updated: August 9, 2026