How to Test JSON APIs Without Writing Code
You do not need a script to sanity-check an API response. Here is a practical, no-code workflow using free browser tools for formatting, validating, and converting JSON.
Most API testing advice starts with "write a script" or "set up a test framework". That is the right end state for a real project, but it skips the first 80% of the job: understanding what the API actually returned. Before you reach for Postman collections or pytest, there is a faster loop that takes about thirty seconds per response and needs nothing installed.
Step 1: Make the response readable
Paste the raw response body into the JSON Formatter. It validates the payload and re-indents it, which immediately answers two questions: is this valid JSON at all, and what is the shape of the data? If the formatter reports an error, read the message carefully - most "the API is broken" reports turn out to be a truncated body or a missing closing brace.
A habit worth building: format the response before you look at it. A 500-character one-liner hides problems that a properly indented tree shows instantly, like an array nested one level deeper than the docs claim.
Step 2: Check the structure, not just the status code
A 200 response with the wrong shape is still a bug. Once the payload is readable, compare it against the documented schema. If your API publishes a JSON Schema, run the response through the JSON Schema Validator and fix the mismatch at the source instead of patching it in every client.
No schema? Then check the boring things manually: are the field names
consistent (camelCase everywhere, not mixed with snake_case)? Are dates in
one format? Is null used for missing values, or is the field
absent? Those inconsistencies are what break frontends weeks later.
Step 3: Convert samples for your tools
The same response usually needs to end up in a different format: CSV for a spreadsheet, YAML for a config file, XML for a legacy consumer. The JSON to CSV and JSON to YAML converters do this without you hand-writing a mapping. They also double as a sanity check: if the conversion produces nonsense, your mental model of the data is probably wrong.
Step 4: Turn a sample into a fixture
When a response looks right, save it. A small library of real API samples - one happy path, one error shape, one empty result - is the fastest way to start a test suite later, and it forces you to actually read the error responses, which are the ones nobody ever tests.
The workflow in one paragraph
Format it, validate it against the schema, convert a copy for whatever consumer needs it, and keep the good samples as fixtures. All of it happens in the browser, so you can paste sensitive-looking response data without worrying about where it is going. When the API is stable enough that you are repeating the same checks ten times a day, that is the signal to automate - and by then you will know exactly which assertions matter.