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Find the Reddit and Quora threads Google shows for your category

By Waggle ·

When someone searches “best specialty coffee roasters in Dubai”, Google often ranks Reddit and Quora threads next to articles. Fenlight's radar, POST /v1/geo/radar, lists the discussion threads, the list articles (“listicles”) and the videos Google shows for a query in a given city, and flags which of them already mention your brand. This guide shows the request, how to read it, what it costs and how far to trust the score.

What the radar returns

  • community_injection_targets: Reddit, Quora and other forum threads, each with its position, title, url, domain, brand_mentioned and an injection_playbook. Despite the field name, the playbook's guidance is to join a thread only if you can genuinely help, to disclose your affiliation and to follow the community's rules on self-promotion.
  • listicle_outreach_targets: pages in the top 20 that are not forums, videos or your own domain and that either rank in the top 10 or have words such as “best”, “top” or “review” in the title or snippet, with brand_mentioned.
  • video_citation_targets: video results, such as YouTube.
  • aeo_question_gaps: People Also Ask questions, with the domain that currently answers each.
  • citation_probability_score and brand_visibility: see the score below.

How it finds the threads

The radar runs two Google searches. The first is your query, to the depth you set (30 by default). The second is the same query with (site:reddit.com/r/ OR site:quora.com) added, one page. Threads from both are merged and de-duplicated by URL. Forum threads are recognised from the address: a fixed list of domains (discussions.apple.com, news.ycombinator.com, quora.com, reddit.com, stackexchange.com, stackoverflow.com, threads.net, tripadvisor.com) and paths such as /comments/, /forum/ and /threads/. The second search adds the Reddit threads (only /comments/ addresses) and Quora pages that the first did not show.

Send the request

Set FENLIGHT_API_KEY to your Vera key, then:

curl -X POST https://fenlight.waggle.ae/v1/geo/radar \
  -H "Authorization: Bearer $FENLIGHT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "query": "best specialty coffee roasters in Dubai",
  "brand": "Sandstone Coffee",
  "domain": "sandstonecoffee.ae",
  "location": "Dubai",
  "gl": "ae",
  "hl": "en",
  "depth": 30
}'

An abridged response, with the real field names (the values are an example):

{
  "engine": "google",
  "query": "best specialty coffee roasters in Dubai",
  "brand": "Sandstone Coffee",
  "domain": "sandstonecoffee.ae",
  "location": {
    "canonical_name": "Dubai,Dubai,United Arab Emirates",
    "gl": "ae",
    "hl": "en",
    "location_code": 1000013
  },
  "citation_probability_score": 25,
  "brand_visibility": {
    "organic_positions": [
      4
    ],
    "in_local_pack": false,
    "local_pack_rank": null,
    "citation_probability_score": 25,
    "score_basis": "google_serp_proxy"
  },
  "community_injection_targets": [
    {
      "position": 1,
      "title": "Best specialty coffee roasters in Dubai?",
      "url": "https://www.reddit.com/r/dubai/comments/…/",
      "domain": "www.reddit.com",
      "tags": [
        "forum_discussion",
        "reddit"
      ],
      "brand_mentioned": false,
      "injection_playbook": "Only participate in this reddit.com thread if you can genuinely help. …"
    }
  ],
  "listicle_outreach_targets": [
    {
      "position": 2,
      "title": "The best coffee roasters in Dubai",
      "url": "https://example.com/…",
      "domain": "www.example.com",
      "brand_mentioned": false
    }
  ],
  "video_citation_targets": [],
  "aeo_question_gaps": [],
  "provenance": {
    "credits_used": 42
  }
}

brand and domain are how the radar knows which results are yours: send at least one, or the score is a fixed baseline that tells you nothing.

What it costs

The radar is two searches, and it asks for the local pack and shopping as well. At depth: 30 that is 3 live pages for the main search (7 credits for the first page and 14 for each page after it) and one page for the thread search at 7 credits, 42 credits, plus 14 for the local pack and 7 for shopping if Google returns them: at most 63 credits, or about $0.018 to $0.034. At depth: 10 it is 14 credits and at most 35. A repeat of the same search by your key within a day is served from its cache for less; send "fresh": true to search live.

Reading the score

A score is built from where your domain ranks, whether you are in the local pack and whether the threads and list articles mention you. Where your best organic result is in the top 3 it adds 35 points, in the top 10 it adds 25, in the top 20 it adds 15 and further down it adds 5. The local pack adds 20 (first place), 15 (second or third) or 10. Being mentioned in a thread adds 5 and in a list article 5. A further 35 points are reserved for appearing in Google's AI Overview, which Fenlight does not measure, so today a score cannot pass 65.

The score is a proxy built from Google results alone: score_basis says google_serp_proxy. It is not a measurement of what any AI assistant answers. For that, use /v1/ai/answers, which is what the first guide in this series covers.

A way to use it that stays honest

  • Run it for the queries your buyers use, in each city you sell in.
  • Open the threads where brand_mentioned is false and read them. Decide whether you can add something useful.
  • If you reply, say who you are and follow the community's rules. Undisclosed promotion breaks platform rules, and in many places endorsement-disclosure laws.
  • For list articles, use the url to find the author and write to them yourself with something worth including.
  • Run it again later with fresh: true to see whether the threads and rankings changed.

Limits to keep in mind

  • The radar finds what Google ranks for that query, in that place, on that day: the top results to your depth and one more page. It does not search Reddit or Quora directly.
  • It reads titles and snippets, not the thread. brand_mentioned looks at the title and snippet only, so a thread that names you further down reads false.
  • It does not report a thread's age, votes or replies, so check these before you decide.
  • A forum that is not in the fixed list may be missed as a thread, though it can still appear among the results.
  • Limits: 60 requests a minute and at most 5 at once, per key.

The MCP server has matching tools: geo_citation_radar is this radar and find_discussion_candidates lists the forum threads in a query's results.