21 Services from agent-product-normalizer.vercel.app

Compares 2 to 5 public product pages and identifies the cheapest offer per currency across all submitted URLs

Ranks search results for a given task/query and flags duplicate, stale, and spam-like entries

Decides whether an AI agent should pause and ask the human for clarification or safely infer and proceed autonomously

Fetches a YouTube transcript and returns a compact all-in-one analysis including context, technical details, and optional question answering for AI agents

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

Fetches a public product page URL and normalizes it into structured, agent-ready commerce JSON

Extracts concrete action items, priority signals, and deadline information from unstructured text such as meeting notes, conversations, or task descriptions.

Detects contradictory facts, negations, and numeric mismatches in a block of text before an agent acts on it

Parses a free-text human request to extract hard constraints, soft preferences, exclusions, budgets, and deadlines into structured form

Compresses long agent conversation history or notes into a compact operational state suitable for agent handoffs

Deduplicates facts and near-duplicate notes from a text block to reduce redundancy and save downstream agent tokens

Computes and returns the delta between two agent handoff states so the receiving agent only processes what changed

Validates a JSON tool call or structured request against a list of required fields and reports which fields are missing

Ranks a list of candidate next actions against the current agent state to recommend the best next step

Scores whether a public product page contains enough reliable, structured data for commerce agents to act on

Scans untrusted text for common prompt-injection and instruction-override attack patterns

Determines whether a failed API or tool call should be retried and recommends the next action to take

Compresses a verbose user task description into concise, search-engine-ready query variants for downstream web or product searches

Redacts common API keys, tokens, and private-key patterns from text before logging or agent handoff

Transforms a messy, ambiguous human request into a structured, execution-ready goal with constraints, ambiguity signals, and at most one clarifying question

Fetches a timestamped transcript from a public YouTube video URL so agents can read video content without watching it