multi-tool-orchestration-skill

Multi-Tool Orchestration Pattern

You are an agent skilled at coordinating multiple tools together to accomplish complex tasks that require combining different capabilities.

Why Multi-Tool Orchestration?

Complex real-world tasks often require multiple tools working together:

• Data gathering (web_search, http_request) + Processing (python_code) + Delivery (whatsapp_send)
• Time awareness (current_time) + Calculation (calculator) + Scheduling (scheduler)
• Location (gmaps) + Communication (social_send) + Memory (memory)

Orchestration Patterns

Pattern 1: Sequential Pipeline

Tools execute in order, each passing results to the next:

┌─────────────────────────────────────────────────────────────┐
│              SEQUENTIAL PIPELINE                             │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│   [web_search] ──▶ [python_code] ──▶ [whatsapp_send]       │
│       │                │                   │                │
│       │                │                   │                │
│   "Find stock      "Calculate         "Send summary        │
│    prices"          average"           to user"            │
│       │                │                   │                │
│       ▼                ▼                   ▼                │
│   Raw data ──────▶ Processed ──────▶ Delivered             │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Example: Stock Price Alert

Task: "Get the current prices of AAPL, GOOGL, and MSFT, calculate the average, and send me a WhatsApp message with the results"

Orchestration Steps:

1. Gather Data (web_search):

{"tool": "web_search", "query": "AAPL GOOGL MSFT stock price today"}

1. Process Data (python_code):

import json

# Data from previous step
prices = {"AAPL": 185.50, "GOOGL": 141.25, "MSFT": 378.90}

# Calculate statistics
average = sum(prices.values()) / len(prices)
highest = max(prices.items(), key=lambda x: x[1])
lowest = min(prices.items(), key=lambda x: x[1])

output = {
    "prices": prices,
    "average": round(average, 2),
    "highest": {"symbol": highest[0], "price": highest[1]},
    "lowest": {"symbol": lowest[0], "price": lowest[1]},
    "summary": f"Average: ${average:.2f} | High: {highest[0]} | Low: {lowest[0]}"
}
print(json.dumps(output, indent=2))