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SignalHarness Prompt Variable Validator
x402
SignalHarness Prompt Variable Validator is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-13).
Validates the variables and structure within a prompt template JSON payload, returning warnings and analysis about the supplied data
Price / call$0.005
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a status field ('succeeded'), a result containing analysis_json with the validation findings, any warnings about the caller-supplied data, the service_id, and an evidence_scope indicating the data was caller-supplied. Also includes a receipt with payment details, request/execution IDs, network info, and a result SHA256 hash.
Example use cases
Pre-flight prompt template check
“Before I send this prompt template to my LLM pipeline, can you validate the variable definitions in this JSON to catch any issues: {"template": "Draft an email to {{recipient}} about {{topic}} in a {{tone}} tone.", "variables": {"recipient": "string", "topic": "string", "tone": "formal"}}?”
Agent workflow prompt validation
“I'm building an agent that fills in prompt templates dynamically — can you run a validation check on this variable JSON to make sure there are no warnings or structural problems before I deploy it: {"variables": [{"name": "company", "type": "string"}, {"name": "deadline", "type": "date"}]}?”
Debugging malformed prompt variables
“Something seems off with my prompt variable JSON and I'm not sure what's wrong — can you analyze and validate this to tell me what warnings or issues come up: {"template": "Summarize {{content}} in {{word_count}} words.", "variables": {"content": null, "word_count": "fifty"}}?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/prompt_variable_validate/invokeWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call validation step for prompt template variable JSON before using it in an agent pipeline. It's particularly useful in automated agent workflows where prompt templates are generated dynamically and need to be sanity-checked for structural correctness before incurring the cost of an LLM call. Prefer this over manual inspection or schema-only validators when you want structured warnings and analysis in a machine-readable format at minimal cost ($0.005 USDC per call).
Side effects
- Malformed or non-JSON request_json input returns a validation error
- Payload exceeding 65536 characters is rejected
- Missing required request_json field causes a schema error
- Network or payment settlement failure may result in no receipt
- Ambiguous variable types may generate warnings rather than hard errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "prompt_variable_validate",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "prompt_variable_validate",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "5000",
"resultSha256": "3e533a77fdc2c5ba8fb607e27963497fbbe5bccc045778e1f483b77bc33665c2",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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