Convert to JSON

Create structured JSON objects from workflow data using key-value pairs.

What is the Convert to JSON Block?

The Convert to JSON block creates structured JSON objects by defining key-value pairs. Use it to format workflow data into a specific JSON structure for APIs, databases, or downstream processing.

Common use cases:

• Create API request payloads
• Structure data for external services
• Format data for Google Sheets or databases
• Build JSON objects from multiple step outputs
• Create standardized data formats
• Prepare data for webhooks
• Transform unstructured data into structured JSON

---

How Does the Convert to JSON Block Work?

1. Add key-value pairs - Define the structure you want
2. Reference workflow data - Use {{step_X.output}} in keys or values
3. Block processes - Replaces placeholders with actual data
4. Returns JSON object - Structured output ready to use

---

Configuration

Key-Value Pairs

Add pairs of keys and values to build your JSON structure.

Key: The property name in the JSON object Value: The data to assign (can be static text or references to previous steps)

Click Add pair to add more key-value pairs.

---

Using Variables

Both keys and values support Liquid templating syntax to reference previous steps.

Basic reference:

Key: keyword
Value: {{step_1.output.Keyword}}

Results in:

{
  "keyword": "crm software"
}

---

Error Handling

Define what happens if the block fails to create JSON.

Options:

1. Terminate Workflow: Stop execution immediately
2. Continue Execution: Proceed to next step with empty output

Select based on whether subsequent steps require the JSON data.

---

Use Cases and Examples

Use Case 1: Create API Payload

Scenario: Format keyword data for an API request

Configuration:

• Key: keyword | Value: {{step_1.output.Keyword}}
• Key: search_volume | Value: {{step_1.output.Search_Volume}}
• Key: difficulty | Value: {{step_1.output.Keyword_Difficulty_Index}}
• Key: timestamp | Value: 2024-01-15

Input from step 1:

{
  "Keyword": "crm software",
  "Search_Volume": "74000",
  "Keyword_Difficulty_Index": "76"
}

Output:

{
  "keyword": "crm software",
  "search_volume": "74000",
  "difficulty": "76",
  "timestamp": "2024-01-15"
}

---

Use Case 2: Combine Multiple Step Outputs

Scenario: Merge data from keyword research and competitor analysis

Configuration:

• Key: target_keyword | Value: {{step_1.output.Keyword}}
• Key: keyword_volume | Value: {{step_1.output.Search_Volume}}
• Key: competitor_domain | Value: {{step_2.output.Domain}}
• Key: competitor_traffic | Value: {{step_2.output.Organic_Traffic}}
• Key: competitor_keywords | Value: {{step_2.output.Organic_Keywords}}

Input from step 1:

{
  "Keyword": "email marketing",
  "Search_Volume": "33100"
}

Input from step 2:

{
  "Domain": "mailchimp.com",
  "Organic_Traffic": "15000000",
  "Organic_Keywords": "456789"
}

Output:

{
  "target_keyword": "email marketing",
  "keyword_volume": "33100",
  "competitor_domain": "mailchimp.com",
  "competitor_traffic": "15000000",
  "competitor_keywords": "456789"
}

---

Use Case 3: Build Webhook Payload

Scenario: Send structured data to external webhook

Configuration:

• Key: event | Value: keyword_analyzed
• Key: keyword | Value: {{step_1.output.Keyword}}
• Key: metrics | Value: {{step_1.output}}
• Key: source | Value: slate_workflow

Output:

{
  "event": "keyword_analyzed",
  "keyword": "crm software",
  "metrics": {
    "Keyword": "crm software",
    "Search_Volume": "74000",
    "Keyword_Difficulty_Index": "76"
  },
  "source": "slate_workflow"
}

---

Best Practices

Naming Keys

• Use consistent naming: snake_case or camelCase
• Be descriptive: target_keyword not just keyword
• Follow conventions: Match API or database requirements
• Avoid special characters: Stick to alphanumeric and underscores

Structuring Data

• Group related data: Use clear, logical organization
• Keep it flat when possible: Avoid unnecessary nesting
• Include metadata: Add timestamps, sources, IDs
• Validate required fields: Ensure all necessary data is included

Using References

• Verify step numbers: Double-check step_X references
• Test with sample data: Run workflow to verify output
• Handle missing data: Use filters with defaults
• Document structure: Comment what each field represents

Common Patterns

Add static metadata:

Key: source
Value: slate_workflow

Key: version
Value: 1.0

Key: generated_at
Value: 2024-01-15

Combine multiple fields:

Key: title
Value: {{step_1.output.Keyword}} - {{step_1.output.Search_Volume}} searches

Apply transformations:

Key: keyword_slug
Value: {{step_1.output.Keyword | downcase | replace: " ", "-"}}

---

Troubleshooting

Output Not Showing Expected Data

Problem: JSON contains {{step_1.output}} instead of actual values.

Solution:

• Verify step number is correct
• Ensure previous step completed successfully
• Check step actually has output data
• Test reference in Text block first

---

Keys Are Empty or Incorrect

Problem: JSON keys are blank or not as expected.

Solution:

• Keys should be static text (use plain text for keys)
• If using dynamic keys, ensure Liquid syntax is correct
• Avoid using complex Liquid in keys

---

Values Not Transforming

Problem: Filters not applying to values.

Solution:

• Check Liquid filter syntax
• Ensure data type matches filter (string/number/array)
• Test filter in Text block first
• See Liquid Templating for correct syntax

---

Invalid JSON Structure

Problem: Output is not valid JSON.

Solution:

• Ensure all keys are unique
• Check for special characters in keys
• Verify all values are properly formatted
• Test with simple static values first

---

Comparing with Code Block

Use Convert to JSON when:

• You need simple key-value mapping
• Structure is straightforward
• No complex logic required
• Quick JSON creation from existing data

Use Code Block when:

• Need complex transformations
• Conditional structure building
• Dynamic key generation
• Nested object creation
• Array processing

---

What’s Next

Now that you understand the Convert to JSON block:

• Transform data with Code Block
• Format text with Text Block
• Send data to APIs with Call API
• Save to Google Sheets
• Learn Liquid Templating for advanced formatting
• Learn about Variable Referencing