JSON Schema validation in Postman - Build an Automation Script

Validate API response against JSON Schema

Preconditions (2)

Step 1: Send a GET request to the API endpoint https://api.example.com/users/1

Step 2: Wait for the response

Step 3: Validate the response body against the provided JSON Schema

✅ Expected Result: The response body matches the JSON Schema and validation passes

Automation Requirements - Postman test scripts

Assertions Needed:

Response status code is 200

Response body matches the JSON Schema

Best Practices:

Use pm.response.to.have.status for status code assertion

Use pm.response.to.have.jsonSchema for schema validation

Keep JSON Schema clear and minimal

Write clear and descriptive test names

Automated Solution

Postman

pm.test('Status code is 200', () => {
    pm.response.to.have.status(200);
});

const userSchema = {
    type: 'object',
    required: ['id', 'name', 'email'],
    properties: {
        id: { type: 'integer' },
        name: { type: 'string' },
        email: { type: 'string', format: 'email' }
    }
};

pm.test('Response matches JSON Schema', () => {
    pm.response.to.have.jsonSchema(userSchema);
});

The first test checks that the response status code is 200, which means the request was successful.

The userSchema defines the expected structure of the JSON response: an object with id as an integer, name as a string, and email as a string in email format.

The second test uses Postman's built-in jsonSchema assertion to verify the response body matches this schema exactly.

This approach ensures the API returns data in the correct format and helps catch errors early.

Common Mistakes - 3 Pitfalls

Not checking the response status code before schema validation

Using an incomplete or incorrect JSON Schema

Hardcoding values in the schema that change frequently

Bonus Challenge

Now add data-driven testing with 3 different user IDs (1, 2, 3) to validate their responses against the JSON Schema

Show Hint

Practice

1. What is the main purpose of JSON Schema validation in Postman?

easy

A. To create new API endpoints

B. To speed up the API response time

C. To change the API response data automatically

D. To check if the API response data matches the expected format

Solution

1. Step 1: Understand JSON Schema validation role

   JSON Schema validation is used to verify that the data structure matches what is expected.

2. Step 2: Relate to Postman usage

   In Postman, it helps confirm the API response format is correct, catching errors early.

3. Final Answer:

   To check if the API response data matches the expected format -> Option D

4. Quick Check:

   JSON Schema validation = format check [OK]

Hint: Remember: JSON Schema validates data format, not speed or changes [OK]

Common Mistakes:

• Thinking it changes API data
• Confusing validation with performance
• Assuming it creates endpoints

2. Which of the following is the correct way to write a JSON Schema type check for a string in Postman test script?

easy

A. "type": "string"

B. "type": string

C. "type": 'string'

D. type: "string"

Solution

1. Step 1: Recall JSON Schema syntax for type

   JSON Schema requires property names and string values to be in double quotes.

2. Step 2: Check each option's syntax

   "type": "string" uses double quotes correctly for both key and value, matching JSON standards.

3. Final Answer:

   "type": "string" -> Option A

4. Quick Check:

   JSON keys and string values need double quotes [OK]

Hint: Use double quotes for keys and string values in JSON Schema [OK]

Common Mistakes:

• Omitting quotes around string values
• Using single quotes instead of double quotes
• Missing quotes around keys

3. Given this Postman test snippet, what will be the test result if the response JSON is {"name": "Alice", "age": 30}?

const schema = {
  type: "object",
  properties: {
    name: { type: "string" },
    age: { type: "integer" }
  },
  required: ["name", "age"]
};

pm.test("Validate response schema", () => {
  pm.response.to.have.jsonSchema(schema);
});

medium

A. Test fails because age is not a string

B. Test passes because response matches schema

C. Test fails because required fields are missing

D. Test fails due to syntax error in schema

Solution

1. Step 1: Compare response data types with schema

   Response has "name" as string and "age" as integer, matching schema types.

2. Step 2: Check required fields presence

   Both "name" and "age" are present in response, fulfilling required array.

3. Final Answer:

   Test passes because response matches schema -> Option B

4. Quick Check:

   Response matches schema = test passes [OK]

Hint: Match data types and required fields to schema for pass [OK]

Common Mistakes:

• Confusing integer with string type
• Ignoring required fields presence
• Assuming syntax error without checking

4. You wrote this JSON Schema in Postman test but the validation always fails:

const schema = {
  type: "object",
  properties: {
    id: { type: "number" },
    email: { type: "string" }
  },
  required: ["id", "email"]
};

pm.test("Check schema", () => {
  pm.response.to.have.jsonSchema(schema);
});

The API response is {"id": 123, "email": "[e-mail]"}. What is the likely problem?

medium

A. The type "number" should be "integer" for whole numbers

B. The required array should be outside the schema object

C. The response keys are missing quotes

D. The test syntax is incorrect

Solution

1. Step 1: Understand JSON Schema number vs integer types

   JSON Schema distinguishes "number" (any number) and "integer" (whole numbers). API returns whole number 123.

2. Step 2: Check if "number" type matches whole number

   "number" accepts integers generally, but Postman JSON Schema validation requires "integer" for whole numbers like 123.

3. Step 3: Consider common Postman issue

   Postman JSON Schema validation is strict; using "number" for integer values can cause mismatch.

4. Final Answer:

   The type "number" should be "integer" for whole numbers -> Option A

5. Quick Check:

   Use "integer" for whole numbers in schema [OK]

Hint: Use "integer" type for whole numbers in JSON Schema [OK]

Common Mistakes:

• Placing required outside schema
• Ignoring quotes in JSON keys (not relevant here)
• Assuming test syntax error without checking

5. You want to validate an API response in Postman where the "tags" field is an array of strings, but it can be empty or missing. Which JSON Schema snippet correctly allows this?

hard

A. {
"type": "object",
"properties": {
"tags": {
"type": ["array", "null"],
"items": { "type": "string" }
}
},
"required": []
}

B. {
"type": "object",
"properties": {
"tags": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["tags"]
}

C. {
"type": "object",
"properties": {
"tags": {
"type": "array",
"items": { "type": "string" }
}
},
"required": []
}

D. {
"type": "object",
"properties": {
"tags": {
"type": "array",
"items": { "type": "string" },
"minItems": 1
}
},
"required": []
}

Solution

1. Step 1: Understand optional field with empty array allowed

   The "tags" field can be missing or present as an empty array or array of strings.

2. Step 2: Analyze each option

   { "type": "object", "properties": { "tags": { "type": "array", "items": { "type": "string" } } }, "required": [] } allows "tags" as array of strings, not required (can be missing). { "type": "object", "properties": { "tags": { "type": "array", "items": { "type": "string" } } }, "required": ["tags"] } requires "tags" always. { "type": "object", "properties": { "tags": { "type": ["array", "null"], "items": { "type": "string" } } }, "required": [] } allows null type which is not needed. { "type": "object", "properties": { "tags": { "type": "array", "items": { "type": "string" }, "minItems": 1 } }, "required": [] } requires at least one item (minItems:1), disallowing empty array.

3. Final Answer:

   { "type": "object", "properties": { "tags": { "type": "array", "items": { "type": "string" } } }, "required": [] } -> Option C

4. Quick Check:

   Optional field with empty array = not required, no minItems [OK]

Hint: Use empty required array and no minItems for optional empty arrays [OK]

Common Mistakes:

• Making field required when optional
• Adding minItems disallowing empty arrays
• Allowing null when not needed