agent-product-normalizer.vercel.app x402 offerings

Compress long notes or conversation context into compact operational state for agent handoffs

Decide whether an API/tool failure should be retried and suggest the next action

Compare 2 to 5 public product pages and select the cheapest offer separately per currency

Score whether a public product page contains enough reliable data for commerce agents

Extract compact price, currency, availability and seller facts from a public product page

Rank search results for a task and flag duplicate, stale and spam-like results

Extract hard constraints, soft preferences, exclusions, budgets and deadlines from a human request

Decide whether an agent should ask the human or safely infer and continue

Report what changed between two agent handoff states so the next agent reads only the delta

Detect contradictory facts, negations and numeric mismatches before an agent acts

Extract concrete action items, priority and deadline signals from messy text

Scan untrusted text for common prompt-injection and instruction-override patterns

Redact common API keys, tokens and private-key patterns before logging or handoff

Rank candidate next actions against the current agent state

Deduplicate facts and near-duplicate notes to save downstream agent tokens

Compress a verbose user task into search-engine-ready query variants

Check whether a tool call or structured request is missing required input fields

Turn a messy human request into an execution-ready goal, constraints, ambiguity signals and one question only if needed

Extract agent-ready context from a public YouTube video: transcript highlights, timestamped key points, chapters, technical tutorial commands, action items, candidate claims, context pack and optional evidence Q&A. Use for summarize YouTube, video-to-context and command extraction workflows.

Normalize a public product page into agent-ready commerce JSON

Extract YouTube transcript, captions and timestamped segments as clean video-to-text for AI agents