search-restaurants

wolt.com/wolt-search-5m1plq · updated May 21, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$browse install wolt.com/wolt-search-5m1plq
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summary

Search Wolt for restaurants in a given city by cuisine, dish, or restaurant name and return a ranked list with name, slug, URL, cuisine tagline, delivery fee, delivery time, price tier, and customer rating. Read-only.

skill.md
name
search-restaurants
title
Wolt Restaurant Search
description
>- Search Wolt for restaurants in a given city by cuisine, dish, or restaurant name and return a ranked list with name, slug, URL, cuisine tagline, delivery fee, delivery time, price tier, and customer rating. Read-only.
website
wolt.com
category
food-delivery
tags
- food-delivery - restaurants - search - wolt - read-only
source
'browserbase: agent-runtime 2026-05-19'
updated
'2026-05-19'
recommended_method
browser
alternative_methods
- method: api rationale: >- Wolt's public REST surface (restaurant-api.wolt.com/v1/*, consumer-api.wolt.com/v1/*) returns 410 Gone with a 'please update the app' body, and v2/v3 routes are 404. The current internal traffic goes through gatekeeper.wolt.com/v1/storefront and /v1/consumer but route names are not enumerable from the SSR HTML and require SPA-minted auth tokens. Confirmed dead-end 2026-05-19 — do not retry.
verified
true
proxies
true

Wolt Restaurant Search

Purpose

Given a city (Wolt city slug + country code) and a free-text query — typically a cuisine ("sushi", "ramen"), dish ("pizza"), or restaurant name — return the ranked list of restaurants that Wolt surfaces for that query on its consumer site, with the name, slug, canonical URL, cuisine tagline, delivery fee, estimated delivery time range, price tier, and customer rating. Read-only — never opens a cart, applies a coupon, or places an order.

When to Use

  • "Find good sushi restaurants in Tel Aviv" / "What ramen places does Wolt deliver in Helsinki?" — agent needs a ranked list of restaurants matching a cuisine or dish in a specific Wolt-served city.
  • Building a comparison table across multiple cuisines (search the same city N times) or across multiple cities (same query, N cities).
  • Pre-checking whether a named restaurant ("Ze Sushi", "Kansai Sushi") is on Wolt before suggesting it to a user.
  • Anywhere you'd otherwise click the magnifying-glass icon in the Wolt UI and type a query. The skill replaces the entire interactive flow with a single URL fetch.

Do not use this skill for menu/dish-level lookup inside a single restaurant (that requires opening the restaurant page and parsing its menu), or for placing orders.

Workflow

Wolt's consumer site exposes a clean URL pattern that performs a search inside a known city without requiring login, delivery-address capture, or cookie state:

https://wolt.com/en/{country_code}/{city_slug}/search?q={url_encoded_query}

The page is client-rendered (Next.js, no server-side data in the initial HTML), so a headless browser must execute JavaScript before the restaurant list appears. The public REST APIs that the legacy mobile clients used (restaurant-api.wolt.com/v1/pages/search, consumer-api.wolt.com/v1/pages/search) now return 410 Gone with a "please update the app" body, and newer gatekeeper.wolt.com/v1/* route names are not publicly enumerable — don't waste turns on direct REST. Drive the page with browse against a Browserbase session with stealth + residential proxy enabled.

  1. Resolve the city slug. Wolt uses ISO-3 country codes plus an English-kebab-case city slug. Common Israeli slugs: tel-aviv, jerusalem, haifa, beer-sheva, eilat, netanya, ramat-gan. Common patterns elsewhere: helsinki, stockholm, berlin, prague, warsaw, athens, budapest, zagreb, tbilisi, tokyo. Validate by opening https://wolt.com/en/{country_code}/{city_slug} first if uncertain — invalid slugs render a 404-style landing page.

  2. Create a stealth + residential-proxy session (Wolt sits behind Cloudflare/Akamai-class fingerprinting; a bare session intermittently gets blocked, especially on rapid follow-up fetches):

    sid=$(browse cloud sessions create --keep-alive --verified --proxies \
      | node -e "let s='';process.stdin.on('data',c=>s+=c).on('end',()=>process.stdout.write(JSON.parse(s).id))")
    export BROWSE_SESSION="$sid"
    
  3. Open the search URL and wait for the client-side render:

    browse open "https://wolt.com/en/isr/tel-aviv/search?q=sushi" --remote
    browse wait timeout 2500 --remote
    

    The wait timeout 2500 is required — the restaurant grid is hydrated 1.5–2.5 s after load fires, and snapshotting earlier yields an empty ## Restaurants and stores section.

  4. Detect the no-results branch first. If the rendered markdown contains the literal heading # No results found, emit the empty-result outcome shape and skip parsing. Wolt surfaces this when the query has zero matches in the city; the rest of the page is just app-download promos and footer links.

  5. Extract structured results from browse get markdown body. Each restaurant renders as a repeating block in this exact order:

    * [![](image_url)](/en/{cc}/{city}/restaurant/{slug})
    {badge}                                ← optional, e.g. "KOSHER", "Vegan friendly"
    {N}₪ delivery fee                      ← N is the delivery fee in local currency
    [{name}](/en/{cc}/{city}/restaurant/{slug})
    {tagline}                              ← cuisine description, e.g. "Asian Sushi Bar"
    {tagline}                              ← line duplicated (mobile/desktop variants in DOM)
    {min}-{max}                            ← delivery time range in minutes
    min
    ₪{min_order}.00$$$$                    ← min order in currency, followed by 1–4 $ price tier
    {rating}                               ← e.g. "8.4" on a 0–10 scale; OMITTED if too few reviews
    

    Parse heuristic: split the markdown on the regex ^\* \[!\[\]\(.+\)\]\((\/en\/[a-z]{3}\/[a-z0-9-]+\/restaurant\/[a-z0-9-]+)\) to get one chunk per restaurant. Within each chunk:

    • slug + canonical URL — captured by the splitter. Canonical URL is https://wolt.com{path}.
    • name — first [name](/en/.../restaurant/{same-slug}) markdown anchor in the chunk.
    • delivery_fee_text — match (\d+)₪ delivery fee (or \d+\.\d+₪ for non-integer fees in EUR markets, where the currency symbol may be ).
    • **time_min/time_max** — match ^(\d+)-(\d+)$immediately followed by amin` line.
    • price_tier — count $ characters in the ₪…$$$$ line (1–4, where $$$$ means top tier).
    • rating — last line of the chunk if it matches ^\d+(\.\d+)?$ and is in [0, 10]. Absent means "not enough reviews" — surface as null, not 0.
    • tagline / cuisine — the duplicated description line. De-duplicate.
    • badges — any non-empty line between the image anchor and the ₪ delivery fee line (e.g. KOSHER).
  6. Default sort is "Recommended" (Wolt's internal score, surfaced as a Sorted byRecommended widget at the top of the list). For "good" / "best" / "top-rated" intent, re-sort client-side by rating DESC, breaking ties by delivery_time_max ASC then delivery_fee ASC. Filter out entries with rating == null first if the user explicitly asked for "good" ratings — those are unrated rather than zero-rated.

  7. Release the session when done:

    browse cloud sessions update "$sid" --status REQUEST_RELEASE
    

Optional enrichment (per restaurant detail page)

If the caller needs full address, opening hours, or menu, navigate to https://wolt.com{slug} for each result. Same stealth/proxy session works. Detail pages render server-side enough that browse get markdown body after wait timeout 2000 returns address + hours reliably. Each enrichment is ~1.5–3 s — budget accordingly when enriching >10 restaurants.

Site-Specific Gotchas

  • /search?q= is the only working URL pattern. Wolt redirects /restaurants?q=sushi to bare /{city}?q=sushi (drops the search context entirely — the query string survives in the URL but no search runs). Always use the explicit /search segment after the city slug.
  • Public REST APIs are dead. restaurant-api.wolt.com/v1/* and consumer-api.wolt.com/v1/* return 410 Gone with body "We've updated the Wolt app! …" (verified 2026-05-19 via residential-proxy fetch from US IPs). restaurant-api.wolt.com/v2/*, restaurant-api.wolt.com/v3/*, consumer-api.wolt.com/v3/* return 404 Not Found. The current internal traffic goes through gatekeeper.wolt.com/v1/storefront and gatekeeper.wolt.com/v1/consumer, but the exact route names aren't enumerable from the SSR HTML and require auth tokens minted by the SPA on page load. Drive the browser; don't try to reverse-engineer the gateway.
  • SSR HTML carries no restaurant data. bb fetch https://wolt.com/en/isr/tel-aviv/search?q=sushi returns 200 OK with ~720 KB of HTML — and zero /restaurant/{slug} anchors in it. The grid is hydrated client-side from a gatekeeper XHR after JS executes. You must use a real headless browser; browse cloud fetch alone is insufficient.
  • City scoping is from the URL only. No IP-based fallback, no cookie state, no "your last city" memory across sessions. If the city slug is wrong (e.g. telaviv without the hyphen), Wolt renders a generic landing page with the country's default city instead of a 404 — silently mis-scoping the search. Always validate the slug if the result count is suspicious (e.g. <5 results for a major cuisine in a city you know is well-served).
  • Anonymous searches return city-wide deliverable restaurants ("TLV - Herzliya area" for Tel Aviv, displayed in the header). The full set returns; once a user sets a specific delivery address, the in-app search filters down to addresses that can be served. The skill operates anonymously by design, so results are a superset of what any individual address would see.
  • Rating absence ≠ rating zero. Restaurants with <~20 reviews omit the trailing rating line entirely. Emit rating: null, not 0 — confusing the two will hide genuinely new highly-rated restaurants and inflate "1 star" filter results.
  • Default sort is "Recommended", not by rating. Wolt's recommendation model blends popularity, sponsored placement, delivery distance, and rating. If the user said "good"/"best"/"top-rated", re-sort by rating client-side and break ties on delivery time + fee.
  • Currency symbol varies by country. Israel uses (NIS), EUR markets show , Nordics show or local symbols. The delivery-fee regex needs to be currency-agnostic: (\d+(?:[.,]\d+)?)\s*[₪€$kr£]\s*delivery fee (and friends). Same for the min-order line in step 5 — the $$$$ price-tier suffix is currency-independent (always literal $), but the leading minimum-order value is local.
  • The $$$$ price tier is always 4 dollar signs literal, with 1–4 of them filled. Don't parse as currency — count the $ characters. (Wolt displays them as light/dark on the page; the markdown extractor returns them all as literal $.) Sometimes the line is ₪0.00$$$$ even though the displayed tier is 2/4 — the leading currency value is the min-order, not the price tier; do not double-count.
  • Tagline lines duplicate. Each restaurant's cuisine description appears twice in the markdown back-to-back. This is a desktop+mobile dual render in the DOM, not a parse bug. De-duplicate.
  • Image proxy domain. Restaurant photos resolve through imageproxy.wolt.com/assets/{id} or imageproxy.wolt.com/mes-image/{uuid}/{uuid}. Both are stable; either is safe to expose to downstream consumers.
  • A non-stealth session sometimes succeeds for the first fetch and then 403s on the next. Wolt's fingerprinting tolerates one cold request but flags consistent headless traits on subsequent calls. Always use --verified --proxies from the start — switching mid-flow does not recover.
  • No formal rate limit observed, but sustained >1 search/sec against the same session causes the page to render with delayed hydration (results appear 4–6 s after load instead of 1.5–2.5 s). Either bump the wait timeout to 6000 ms under load, or pace requests to ≤ 0.5/s. Verified during iter-1 with back-to-back Tel Aviv + Jerusalem queries.

Expected Output

Three distinct outcome shapes.

Results returned

{
  "success": true,
  "city": { "country_code": "isr", "slug": "tel-aviv", "display_name": "TLV - Herzliya area" },
  "query": "sushi",
  "result_count": 50,
  "sorted_by": "recommended",
  "restaurants": [
    {
      "name": "Ze Sushi | Bazel",
      "slug": "ze-sushi-bazel",
      "url": "https://wolt.com/en/isr/tel-aviv/restaurant/ze-sushi-bazel",
      "image_url": "https://imageproxy.wolt.com/mes-image/9b0cc273-2d6f-4e2a-abb8-90bfd27a6fd9/af0cf33c-ad30-43b4-b08d-84107843f8db",
      "tagline": "Classic Japanese Sushi Since 2004",
      "badges": [],
      "delivery_fee": { "amount": 0, "currency": "ILS", "display": "0₪" },
      "delivery_time_min_minutes": 30,
      "delivery_time_max_minutes": 40,
      "min_order": { "amount": 0, "currency": "ILS", "display": "₪0.00" },
      "price_tier": 2,
      "rating": 8.0
    },
    {
      "name": "Kansai Sushi | Tel Aviv",
      "slug": "kansai-sushi",
      "url": "https://wolt.com/en/isr/tel-aviv/restaurant/kansai-sushi",
      "image_url": "https://imageproxy.wolt.com/assets/67332fbac59f3326de5432dd",
      "tagline": "The sushi of modern Japan | Kosher Chief Rabbinate Tel Aviv",
      "badges": ["KOSHER"],
      "delivery_fee": { "amount": 0, "currency": "ILS", "display": "0₪" },
      "delivery_time_min_minutes": 35,
      "delivery_time_max_minutes": 45,
      "min_order": { "amount": 0, "currency": "ILS", "display": "₪0.00" },
      "price_tier": 3,
      "rating": 8.2
    }
  ]
}

No results

{
  "success": true,
  "city": { "country_code": "isr", "slug": "tel-aviv", "display_name": "TLV - Herzliya area" },
  "query": "zzzqqqxxxx",
  "result_count": 0,
  "restaurants": [],
  "reason": "no_results"
}

Invalid city / slug not recognized

{
  "success": false,
  "reason": "invalid_city_slug",
  "attempted_url": "https://wolt.com/en/isr/telaviv/search?q=sushi",
  "hint": "Wolt slugs are kebab-case English. Try 'tel-aviv' (with hyphen). Validate with /en/{cc}/{slug} before searching."
}
how to use search-restaurants

How to use search-restaurants on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add search-restaurants
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$browse install wolt.com/wolt-search-5m1plq

The skills CLI fetches search-restaurants from GitHub repository wolt.com/wolt-search-5m1plq and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/search-restaurants

Reload or restart Cursor to activate search-restaurants. Access the skill through slash commands (e.g., /search-restaurants) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.642 reviews
  • Zara Singh· Dec 16, 2024

    search-restaurants fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chaitanya Patil· Dec 8, 2024

    I recommend search-restaurants for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yusuf Yang· Dec 8, 2024

    search-restaurants is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ava Verma· Dec 4, 2024

    Keeps context tight: search-restaurants is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Piyush G· Nov 27, 2024

    Useful defaults in search-restaurants — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Luis Patel· Nov 27, 2024

    Solid pick for teams standardizing on skills: search-restaurants is focused, and the summary matches what you get after install.

  • Tariq Mehta· Nov 15, 2024

    Registry listing for search-restaurants matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ava Smith· Nov 7, 2024

    We added search-restaurants from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ira White· Oct 26, 2024

    Solid pick for teams standardizing on skills: search-restaurants is focused, and the summary matches what you get after install.

  • Shikha Mishra· Oct 18, 2024

    search-restaurants has been reliable in day-to-day use. Documentation quality is above average for community skills.

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