Data Validation Node

The Data Validation Node checks incoming data against a JSON Schema before passing it to downstream nodes. This ensures only well-structured, expected data flows through your pipeline, reducing errors and improving reliability.

Configuration

The settings dialog has two tabs, one per validated channel:

Payload Schema

Enter a JSON Schema to validate the incoming payload. This tab is required — an empty schema here blocks all output (highlighted on the node until set).

Example

{
    "type": "object",
    "properties": {
        "products": {
        "type": "array",
        "items": {
            "type": "object",
            "required": ["id", "title", "price"],
            "properties": {
            "id": { "type": "integer" },
            "title": { "type": "string" },
            "price": { "type": "number" }
            }
        }
        }
    },
    "required": ["products"]
}

Tools Payload Schema

Optionally enter a second JSON Schema to validate the incoming toolsPayload. Leave it empty to skip toolsPayload validation entirely.

A "Block output if Tools Payload is invalid" switch controls what happens on failure:

• Off (default): payload still forwards downstream; the (invalid) toolsPayload is simply dropped. The failure is still visible in the Logs panel.
• On: a toolsPayload failure blocks the node's entire output, exactly like a payload failure.

Configuration Schema

This is the full JSON Schema for the node's data configuration object, generated from the Zod schema used to validate the node at runtime.

{
  "type": "object",
  "properties": {
    "validatePayload": {
      "type": "boolean",
      "description": "When on (default) or unset, payload is validated against payloadSchema. When off, payload passes through unvalidated."
    },
    "payloadSchema": {
      "type": "string",
      "description": "JSON Schema string used to validate the incoming payload. Left empty, payload validation is skipped (permissive)."
    },
    "validateToolsPayload": {
      "type": "boolean",
      "description": "When on, toolsPayload is validated against toolsPayloadSchema. When off/unset, toolsPayload passes through unvalidated."
    },
    "toolsPayloadSchema": {
      "type": "string",
      "description": "JSON Schema for toolsPayload, used when validateToolsPayload is on. Left empty, toolsPayload validation is skipped."
    }
  },
  "required": [
    "payloadSchema"
  ],
  "additionalProperties": false
}

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Example Usage

For example usage, see the Product Data Validation and Visualization workflow, which demonstrates fetching API data, validating it, and visualizing the results.

Common Use Cases

1. API Response Validation: Ensure third-party API responses match expected formats.
2. Input Sanitization: Prevent malformed or incomplete data from entering your workflow.
3. Data Quality Assurance: Enforce required fields and data types.
4. Error Prevention: Catch issues early and provide feedback for correction.

Best Practices

• Test your schema: Use tools like JSON Schema Validator to verify your schema.
• Be specific: Define required fields and types to catch subtle errors.
• Handle errors: Use downstream nodes to handle validation failures gracefully.
• Keep schemas up to date: Update schemas as your data structures evolve.

Troubleshooting

Common Issues

• Invalid schema: Ensure your schema is valid JSON and follows the JSON Schema standard.
• Unexpected validation errors: Double-check that incoming data matches the schema.
• Empty or missing fields: Use required properties to enforce presence of important fields.

Performance Tips

• Use for small to medium-sized data objects.
• For large datasets, validate only the necessary parts to improve performance.
• Test with sample data before running full workflows.

Supported Data Formats

• Input: Any JSON object or array, for both payload and toolsPayload.
• Output:
  • Payload invalid → no output; an error is sent as feedback and shown in the Logs panel.
  • Payload valid, toolsPayload invalid → payload forwards, toolsPayload is dropped, unless "Block output if Tools Payload is invalid" is on, in which case it blocks like a payload failure.
  • Both valid (or toolsPayload schema unset) → payload and toolsPayload forward unchanged.

Why Use Data Validation?

Validating data before processing ensures that only well-structured, expected data flows through your pipeline. This reduces errors, improves reliability, and makes debugging easier—especially when working with third-party APIs.