Direct Spend Visualizer

Direct Spend Visualizer

Overview

This skill enables visualization of FeedMob direct spend data as ASCII line charts. It fetches data using the FeedMob MCP API and displays net spend and gross spend trends over time in an easy-to-read format. All spend values are reported in USD currency.

When to Use This Skill

Use this skill when users request:

• Visualization of direct spend data for specific click URL IDs
• Line charts showing spend trends over time
• Comparison of net spend vs gross spend
• Visual representation of FeedMob campaign spending

Example user requests:

• "Show me a chart of direct spend for click_url 22271"
• "Visualize the spending for the last 30 days"
• "Display spend trends for multiple campaigns"
• "Chart the direct spend data"

Workflow

Step 1: Fetch Direct Spend Data

Use the FeedMob MCP tool to retrieve direct spend data:

mcp__feedmob__get_direct_spends

Required parameters:

• start_date: Start date in YYYY-MM-DD format
• end_date: End date in YYYY-MM-DD format
• click_url_ids: Array of click URL IDs (as strings)

Example:

{
  "start_date": "2025-11-07",
  "end_date": "2025-11-14",
  "click_url_ids": ["22271", "22272"]
}

Step 2: Save the API Response

Save the JSON response from the API to a temporary file. This allows the visualization script to process the data.

Example:

import json

# API response data
api_response = {
  "status": 200,
  "data": [
    {
      "feedmob_click_url_id": 22271,
      "campaign_name": "Campaign_Name",
      "date": "2025-11-07",
      "feedmob_net_spend": 119.92,
      "feedmob_gross_spend": 78.00
    }
    # ... more records
  ]
}

# Write to temp file
with open('/tmp/direct_spend_data.json', 'w') as f:
    json.dump(api_response, f)

Step 3: Visualize the Data

Execute the visualization script to generate ASCII line charts:

python scripts/visualize_direct_spend.py /tmp/direct_spend_data.json

The script can also read from stdin:

echo '{"data": [...]}' | python scripts/visualize_direct_spend.py

Step 4: Present the Results

The script outputs:

1. ASCII line chart - Visual representation of spend trends with Y-axis values in USD
2. Legend - Explains chart symbols (N = Net Spend, G = Gross Spend)
3. Data table - Detailed daily breakdown with totals, all values formatted in USD

Display this output to the user directly.

Script Reference

visualize_direct_spend.py

Located in scripts/visualize_direct_spend.py, this script:

Input: JSON data from mcp__feedmob__get_direct_spends

Output: ASCII line charts with:

• Scaled line chart showing net and gross spend trends
• Y-axis with spend values in USD (formatted with $ symbol)
• X-axis with date labels
• Detailed data table with totals (all values in USD)
• Summary statistics

Features:

• Handles multiple click URL IDs (creates separate charts)
• Automatically scales to data range
• Shows both net and gross spend on same chart
• Provides detailed numeric breakdown

Usage:

# From file
python scripts/visualize_direct_spend.py data.json

# From stdin
cat data.json | python scripts/visualize_direct_spend.py

Example Complete Workflow

User request: "Show me direct spend for click_url 22271 for the last 7 days"

1. Calculate date range (today minus 7 days to today)
2. Call mcp__feedmob__get_direct_spends with:
   • start_date: "2025-11-07"
   • end_date: "2025-11-14"
   • click_url_ids: ["22271"]
3. Save response to /tmp/direct_spend.json
4. Execute: python scripts/visualize_direct_spend.py /tmp/direct_spend.json
5. Display the resulting chart and table to the user

Tips

• For large date ranges, the chart may become crowded. Consider breaking into smaller periods.
• The script supports multiple click URL IDs and will create separate charts for each.
• If no data is returned from the API, inform the user that no spend data exists for the specified parameters.
• Date format must be YYYY-MM-DD for both the API and the script.
• All spend values from FeedMob are in USD currency and will be displayed with the $ symbol.

Converted and distributed by TomeVault — claim your Tome and manage your conversions.