When a front end depends on an API that does not exist yet, development stalls. Designers cannot check a layout without rows to display, and developers cannot test an empty state, a long list, or a missing field without data to feed the components. Mock data fills that gap. It is example data shaped like the real response, generated from the schema rather than typed out by hand. Done well, it lets the whole team work in parallel: the API team builds the server, while the front-end team builds against data that already has the right shape.
The GraphQL Mock Data Generator tool takes a GraphQL schema you paste in and produces example JSON from it. You do not need a running server, an account, or a network connection to the API. You paste the type definitions, and the tool reads the fields and their types, then fills each one with a plausible sample value. The result is ordinary JSON you can copy into a test fixture, a storybook story, or a prototype.
How the tool works
A GraphQL schema describes types, and each type has fields with types of their own. A User type might have an id of type ID, a name of type String, an age of type Int, and an active flag of type Boolean. The generator walks through the query you describe and, for each field, picks a sample value that matches the declared type. Strings become short example text, integers become ordinary numbers, booleans become true or false, and nested object types are expanded into nested objects. List types produce a small list, so you can see how a component handles more than one item.
This approach has a useful property: the mock data cannot drift far from the schema, because it is derived from it. If the schema says a post has a title and an author, the mock post will have a title and an author. If you rename a field in the schema and regenerate, the mock output follows the rename automatically. That makes regeneration cheap, which matters because schemas change often in early development. Rather than hand-editing twenty fixtures after every rename, you paste the updated schema and generate again.
The tool is deliberately simple about values. It does not know your business rules. It cannot tell that an email field should look like an email address, that a price should never be negative, or that a published date should come before today. It fills each field with a generic sample for its type. That is enough to test layout, loading states, and data flow, but it is not enough to test validation logic, and it should never be mistaken for realistic production data.
A worked example
Suppose your schema defines a query with two fields, user of type User and count of type Int, and a User type with id, name, age, and active fields. Paste that schema into the tool and generate. The output is a JSON object with a user object and a count. The user object contains an id such as "id-1", a name such as "Example name", an age of 42, and active set to true, while count is 42. The exact sample values are illustrative, but the structure is the point: every field declared in the schema appears in the output, with a value of the right type.
Now imagine building a profile card with this output. You can check that a long name wraps correctly, that the age displays beside the name, and that the active flag drives the right badge. If the card looks broken with two items in a list, generate a list field and confirm the layout holds. Because the generator produces two items for a list type such as a list of posts, you get a quick sense of repeated content without hand-writing a second entry.
A second habit makes mocks more useful: generate once, save the output as a fixture, and keep the schema beside it. When the schema changes, regenerate and compare the new output with the old fixture. Fields that disappeared or changed type will show up in the comparison, and those are exactly the places where the front end may need updating. Treat the difference as a checklist, not as a surprise discovered after deployment.
Common mistakes
The first mistake is treating mock values as realistic. A sample age of 42 tells you nothing about how your code handles an age of zero, a negative age, or a missing age. Write separate test cases for those edges; do not assume the generator has covered them. The second mistake is mocking only the happy path. Real APIs return empty lists, null fields, and error responses. Generate the happy path for layout work, then hand-write the empty and error cases, because those are where user interfaces most often break.
A third mistake is letting mocks live so long that nobody remembers they are mocks. Label fixture files clearly, and keep a note of which schema version generated them. When the real API arrives, replace the fixtures with real responses in at least one end-to-end test. If a component only ever passed against mock data, you have tested the component against your own assumptions, not against the system it will actually talk to.
A fourth mistake is pasting a schema with field arguments and expecting the generator to honour them. This tool ignores field arguments and treats custom scalar types as sample strings. That is a documented simplification, not a bug to work around by pasting ever more elaborate schemas. If an argument changes the result in an important way, such as a filter that returns a subset, model that case by hand.
Honest limitations
This tool generates example JSON from a pasted schema in your browser. It does not run a GraphQL server, execute resolvers, validate the schema against a specification, or fetch data from any API. It does not check that your schema is internally consistent, and it will not warn you if two types contradict each other. Custom scalars are filled with plain sample strings, field arguments are ignored, and fragment spreads in queries are not the focus of this generator. For those deeper checks, use a schema validator or a real server implementation. Also remember that generated data may include values that would be invalid in production, such as an age that does not fit your domain, so never load mock output into a production database.
Closing
Mock data is a scaffold, not the building. Use it to move fast while the API is still being built, test the edges by hand, and replace it with real responses as soon as you can. For the next steps in the same workflow, estimate how expensive a query will be with the GraphQL Query Complexity Estimator tool, and generate example payloads for REST-style APIs with the OpenAPI Mock Data Generator tool. If you are new to shaping JSON itself, our guide How to Format and Validate JSON explains how to read, check, and tidy the output before you use it.