PayloadForge documentation

PayloadForge needs to understand the API request you want to create. You can configure the HTTP method, endpoint and request headers manually, or start from an existing cURL request.

Importing cURL is useful when you already have a working request from API documentation, another developer, a browser developer tool or an API client.

curl --request POST \
  --url 'https://api.example.com/v1/companies' \
  --header 'Authorization: Bearer {{API_TOKEN}}' \
  --header 'Content-Type: application/json'

The target structure describes the fields expected by the API. An example JSON object can be used to infer field names and basic data types.

{
  "company_name": "Microsoft",
  "country_code": "US",
  "revenue": 245000,
  "active": true
}

PayloadForge currently works with basic values such as strings, numbers, booleans and null values. Richer schema constraints are planned for future versions.

CSV data can be imported into the Request Data table. Each source column becomes a column PayloadForge can map to the target API, while each row represents one output object.

Spreadsheet headers and API property names frequently differ. Mapping tells PayloadForge which source value belongs in each target field.

Straightforward names can be matched automatically. Mappings that cannot be established safely remain available for manual selection.

A value that looks acceptable in a spreadsheet may still be invalid for an API. For example, the text₹3,83,000is not directly a JSON number.

PayloadForge validates values against the configured field type before generating the final request.

Transformations clean or normalize source values before type conversion and final validation. Multiple rules can be combined into an ordered pipeline.

For example,₹3,83,000can be transformed by removing the currency symbol and comma separators before converting the result to a number.

A rule that fixes one row may fail on another. PayloadForge therefore evaluates the transformation pipeline across the entire column.

Dataset Impact reports how many rows are valid, how many remain invalid and sample transformed values. Invalid rows are prioritized so problems can be found quickly.

PayloadForge can detect common formatting patterns and propose transformations that improve the dataset.

Numeric suggestions can identify currency symbols, thousands separators and surrounding whitespace. Boolean suggestions can normalize common labels such as Yes, No, Active, Inactive, Enabled and Disabled.

Suggestions are simulated before they are applied. A proposed cleanup must improve validation before it is presented as a useful suggestion.

PayloadForge checks field mappings and transformed values before the request is considered ready. Invalid number or boolean values are surfaced with the row and field responsible for the problem.

When the request is valid, PayloadForge generates typed JSON and a cURL representation using the configured HTTP method, endpoint and headers.

[
  {
    "company_name": "Apple",
    "country_code": "US",
    "revenue": 383000,
    "active": true
  }
]