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Eval Engine API — Pay-per-call AI Evaluation
x402
Eval Engine API — Pay-per-call AI Evaluation is a paid API for AI agents from eval.zuluworksai.com, paid per call via x402, $0.005/call, status down (last checked 2026-09-15).
Scores LLM outputs, agent trajectories, and model responses against benchmark rubrics for automated AI quality evaluation
Price / call$0.005
Success—
StatusDown
How this service works
Pay-per-call AI evaluation engine. Score LLM outputs, agent trajectories, and model responses against benchmark rubrics. $0.005 per eval via x402 USDC on Base. Free trial available.
Output
Returns a JSON-RPC response object containing evaluation results including scores, rubric assessments, and quality ratings for the submitted LLM output or agent trajectory. The result field contains the structured evaluation with numeric scores and qualitative feedback per benchmark dimension.
Example use cases
Agent trajectory quality gate
“I need to evaluate whether my customer support agent handled this 10-turn conversation correctly — can you run it through an eval rubric that checks for helpfulness, accuracy, and appropriate escalation behavior and give me a score?”
LLM output regression testing
“Before I deploy my new fine-tuned model, score these 5 sample responses against my benchmark rubric for tone, factual accuracy, and conciseness so I can see if it regressed from the previous version.”
Automated RAG answer grading
“Grade this RAG pipeline answer against the ground truth: the question was 'What is our refund policy?' and the model responded with a 3-sentence answer — evaluate it for faithfulness to the source document and completeness.”
Technical details
Endpoint
POSThttps://eval.zuluworksai.com/mcpWhen to prefer
Choose this endpoint when you need pay-per-call, on-demand AI evaluation without committing to a subscription — ideal for CI/CD pipelines, spot-checking model outputs, or low-volume evaluation tasks. Best suited for teams that want to pay only for evals they run ($0.005 per call via USDC on Base) and need MCP-compatible tooling that integrates with agent frameworks. Prefer over batch eval platforms when you need real-time scoring in an agent loop.
Side effects
- Payment failure if insufficient USDC balance on Base — returns 402 Payment Required
- Invalid JSON-RPC format causes parse error response with error object
- Missing required fields (id, method, jsonrpc) returns validation error
- Unknown MCP method returns method-not-found error
- Malformed agent trajectory or LLM output may return low-confidence evaluation or error
- Network timeout on complex trajectory evaluations
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| idrequired | string | |
| methodrequired | string | MCP method (tools/call, tools/list, etc.) |
| params | object | |
| jsonrpcrequired | string |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| id | string | |
| error | object | |
| result | object | |
| jsonrpc | string |
Tags
x402
Reviews
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Zero review prompt (fires after every successful call)
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