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Context Preparation API — Inspect
x402
Context Preparation API — Inspect is a paid API for AI agents from nexus-x402-ff2a.onrender.com, paid per call via x402, $0.0002/call, status unknown (last checked 2026-09-14).
Runs deterministic safety inspection, multi-format cleaning, structural chunking, hybrid ranking, contradiction detection, and target-model token packing on text content for AI agent pipelines.
Price / call$0.0002
Success—
StatusUnknown
How this service works
Deterministic multi-format cleaning, safety inspection, structural chunking, hybrid ranking, contradiction checks, and target-model token packing for AI agents.
Output
Returns a JSON object with: the operation type performed ('clean', 'inspect', 'pack', or 'prepare'), the processed content string, an array of structural chunks (each as an object), a safety array listing any detected issues, a warnings array, a metrics object with processing statistics, a target object describing model context details, and an optional potentialContradictions array listing detected conflicting statements across documents.
Example use cases
RAG pipeline document pre-processing
“I have 10 retrieved documents and a user question about climate policy — can you clean them, rank the chunks by relevance using the query embedding I'll provide, check for contradictions between them, and pack everything into a 3000-token context for my model?”
Safety screening before LLM inference
“Before I send this user-submitted text to my AI assistant, can you run a full safety inspection and flag any harmful content, PII, or policy violations, with redaction enabled?”
Structured chunking for long-document QA
“I have a 20-page legal contract and I need to answer specific questions about it — please chunk it into 256-token pieces, rank them by relevance to 'what are the termination clauses?', and pack the top chunks into a 2048-token window.”
Technical details
Endpoint
POSThttps://nexus-x402-ff2a.onrender.com/v1/inspectWhen to prefer
Choose this endpoint when you need a deterministic, auditable pre-processing pipeline for AI agent context windows — especially when you require safety inspection, contradiction detection, and token-budget-aware packing in a single call. Prefer it over ad-hoc chunking libraries when you need hybrid ranking (semantic + structural) and need the output validated against a specific target model's constraints. It is particularly well-suited for RAG pipelines, long-document QA, and any agentic workflow where untrusted or multi-source documents must be safely ingested before LLM inference.
Side effects
- Missing required 'content' or 'documents' field returns a validation error
- Invalid or out-of-range maxTokens or chunkTokens integers cause processing failure
- Malformed queryEmbedding array (wrong dimensions) causes ranking step to fail
- Payment not included or insufficient — x402 payment of 0.0002 USDC required per call
- Oversized input exceeding model limits may result in truncation warnings or errors
- Network timeout on the Render-hosted service during cold start
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| redact | boolean | Defaults to true for /prepare and false for other operations. |
| target | object | |
| content | string | |
| question | string | |
| documents | array | |
| maxTokens | integer | |
| chunkTokens | integer | |
| queryEmbedding | array |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| chunksrequired | array | |
| safetyrequired | array | |
| targetrequired | object | |
| contentrequired | string | |
| metricsrequired | object | |
| warningsrequired | array | |
| operationrequired | — | |
| potentialContradictions | array |
Tags
x402
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