Nero Labs Review Analyzer

x402

Nero Labs Review Analyzer is a paid API for AI agents from x402-agent-tools.nerolabs.workers.dev, paid per call via x402, $0.03/call, status unknown (last checked 2026-10-22).

Analyzes up to 50 customer reviews with AI to extract sentiment, complaint/praise themes, urgency, and optionally drafts owner replies and a business summary report

Price / call$0.03

Seller volume →—

Success—

StatusUnknown

How this service works

Pay-per-call tools for AI agents, 31 in all: read web pages and RSS feeds, OCR, PDF text, transcription; email, phone and domain checks; UK, Irish and Polish company lookups and UK risk reports; website contacts, tech stack and SEO audits; PageSpeed; AI review, job post and row enrichment; data cleaning, joins, dedupe and diffs; US and EU compliance rules. No account or API key: pay each call in USDC on Base over x402.

Output

Returns an array of row objects where each review row includes: sentiment label (positive/negative/mixed), a key quote, a review summary, praise and complaint themes (with categories), analysis status, and optionally a ready-to-post owner reply. If business reports are requested, additional report-type rows are included summarizing top issues per business. Each row has a rowType field to distinguish review rows from report rows.

Example use cases

Monthly review digest for a restaurant

“I have 40 new Google reviews for The Harbour Kitchen this month — can you analyze them all, group the top complaints and praise points, and give me a business report I can share with my manager? Use a warm tone for any reply drafts.”

Detecting recurring complaints for a hotel chain

“Here are 50 recent TripAdvisor reviews across our three hotels. Can you analyze them using these custom themes — 'cleanliness', 'check-in', 'breakfast', 'noise', 'value' — so I can compare issues month to month? Just give me the review analysis, skip the business report.”

Drafting owner replies to negative feedback

“I have 15 customer reviews for our dental practice, some are quite negative. Can you analyze the sentiment and draft a professional owner reply for each one? Add this line to any negative replies: 'Please call us on 01234 567890 so we can put this right.'”

Technical details

Endpoint

POSThttps://x402-agent-tools.nerolabs.workers.dev/ai/review-analyze?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need structured AI analysis of customer review text at scale — especially when you want theme tagging (custom or auto-generated), sentiment classification, and optionally ready-to-post owner replies in a single call. It is purpose-built for reputation management workflows, supports batch processing up to 50 reviews, and provides per-review cost transparency. Prefer it over generic LLM prompting when you need consistent, comparable output across multiple runs with fixed theme taxonomies.

Side effects

Batch exceeds 50 reviews — request rejected or truncated
Invalid output enum value — returns validation error
Reply draft violates rules (no refunds/fault admission) — rewritten once then flagged
Missing or empty reviewTexts array — returns error or empty result
Reviews in unsupported language may reduce analysis accuracy
Network timeout on very large batches with reply drafts enabled

Input

Schema · Request body

Field	Type	Description
output	string	What to return. 'Reviews and business reports' gives one analysed row per review plus one report row per business (rowType tells them apart). 'Reviews only' skips the reports. 'Business reports only' returns just the report rows; every review is still analysed and charged, because the report is buil
themes	array	Leave empty and the AI names each complaint and praise point in a few plain words. Or give your own list (for example 'waiting time', 'staff', 'cleanliness', 'price') and every point is tagged with one of yours, so runs stay comparable month to month.
replyTone	string	How the replies should sound.
replyDrafts	boolean	Adds a ready-to-post owner reply to each review (charged per reply, on top of the review). Replies thank the customer, respond to their actual points, and never promise refunds, discounts or compensation, never admit fault and never invent facts. A draft that breaks a rule is rewritten once, then wi
reviewTexts	array	A plain list of reviews pasted as text, one per entry, for a quick run with no file. Use a dataset, file or Google Sheet to keep star ratings, dates and your own columns.
businessName	string	Name to use when your data has no business column (for example pasted review texts or one company's export). Reviews are grouped into one report per business; with no business column and no name they are reported together as 'All reviews'.
replyContact	string	Optional line added, exactly as written, to replies to negative reviews and to mixed reviews of 3 stars or less, for example 'Please call us on 01234 567890 so we can put this right.' Without it, replies to complaints just invite the customer to get in touch, and never invent a phone number or email
replySignOff	string	Added at the end of every reply, for example 'The Riverside Cafe team' or 'Sam, Owner'. Leave empty for no sign-off.
replyLanguage	string	Leave as 'same' to reply in each review's own language, or name one language (for example 'English' or 'Spanish') for every reply.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "rows": [
      {
        "rowType": "review",
        "keyQuote": "Great food but we waited 40 minutes for a table and nobody apologised",
        "sentiment": "mixed",
        "reviewDate": null,
        "reviewText": "Great food but we waited 40 minutes for a table and nobody apologised.",
        "reviewStars": null,
        "praiseThemes": [
          "great food"
        ],
        "reviewSummary": "The food was great but there was a long wait for a table and no apology from staff.",
        "analysisDetail": null,
        "analysisStatus": "ok",
        "reviewBusiness": "Example Bistro",
        "complaintThemes": [
          "long wait time",
          "no apology for wait"
        ],
        "praiseCategories": [
          "food_and_drink"
        ],
        "complaintCategories": [
          "speed_and_waiting",
          "staff_attitude"
        ]
      }
    ],
    "count": 1,
    "_example_note": "Real output, trimmed: long lists cut to their first items, fields and text shortened."
  }
}

Reviews

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Run ID: run_7f3a9c2e
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