Quora Keyword Search & Author Discovery Scraper: MCP config, n8n workflow and API

{
  "mode": "urls",
  "directUrls": [
    "https://www.quora.com/What-is-Python-primarily-used-for"
  ],
  "resultTypes": [
    "question",
    "answer"
  ],
  "includeAnswers": true,
  "maxAnswersPerQuestion": 2,
  "includeRelatedQuestions": false,
  "maxRelatedQuestions": 0,
  "includeAuthorProfiles": false,
  "maxAuthors": 20,
  "filterAiAnswers": "include",
  "answerSortOrder": "observed",
  "includeMedia": false,
  "includeOutboundLinks": false,
  "includeAnswerHtml": false,
  "includeProfileSensitiveFields": true,
  "includeSocialLinks": true,
  "maxItems": 3,
  "maxItemsPerSource": 0,
  "includeFieldEvidence": true,
  "onlyUnanswered": false
}

---
name: quora-keyword-search-and-author-discovery-scraper
description: "Quora questions, full answers, author profiles, topics, and access-status rows. Runs the Apify Actor vexaloom-labs/quora-keyword-search-and-author-discovery-scraper. Use when the user needs Quora data with fields such as content_type, title, url, question_id, answer_id, author_name and an Apify token is available."
compatibility: "Needs network access, curl, and an APIFY_TOKEN environment variable. Runs are billed to the token owner's Apify account."
metadata:
  actor: "vexaloom-labs/quora-keyword-search-and-author-discovery-scraper"
  store-url: "https://apify.com/vexaloom-labs/quora-keyword-search-and-author-discovery-scraper"
---

# Quora Keyword Search & Author Discovery Scraper

Use this skill to fetch Quora data with the Apify Actor `vexaloom-labs/quora-keyword-search-and-author-discovery-scraper` and work with the returned rows.

## Before running

1. Check that `APIFY_TOKEN` is set in the environment. Never print it or write it to a file.
2. Build `input.json` from the user's request using the fields below. Keep limits small for a first run.
3. Confirm the search terms or URLs with the user if the request is ambiguous.

## Input fields

| Field | Type | Meaning |
| --- | --- | --- |
| `mode` | one of `urls`, `search`, `authors` | What to scrape. `urls` follows pasted Quora links. `search` discovers indexed question links for your keywords and expands related questions. `authors` builds author profiles from pasted profile or question URLs. |
| `directUrls` | array | Paste question, answer, profile, or topic URLs. Language subdomains such as es.quora.com are accepted. Invalid links are reported as status rows. |
| `searchQueries` | array | Best-effort keyword discovery. Quora's own search page is gated for anonymous visitors, so results depend on the public web index; a run with no indexed matches returns a clear status row instead of invented results. |
| `resultTypes` | array | Which entity rows to emit. Status and run-manifest rows are always written when relevant. |
| `includeAnswers` | boolean | Emit answer rows and fetch answers beyond the first visible cards on question pages. |
| `maxAnswersPerQuestion` | integer | Answer cap for each question URL. Zero emits no answers from question pages. |
| `includeRelatedQuestions` | boolean | Follow the related-question links on each question page and scrape those questions too. This is the reliable discovery path when keyword search is gated. |
| `maxRelatedQuestions` | integer | Related questions retained per question page and used as discovery seeds. Zero keeps all observed related questions. |
| `includeAuthorProfiles` | boolean | Open each discovered author's profile to add followers, following, answer/question counts, views, join date, and credential. |
| `maxAuthors` | integer | Maximum unique author rows emitted from answer evidence. |
| `filterAiAnswers` | one of `include`, `exclude`, `only` | Include all answers, exclude answers explicitly labelled as AI, or keep only those AI-labelled answers. |
| `answerSortOrder` | one of `observed`, `upvotes_desc`, `newest` | Keep Quora's observed order, or sort by explicit upvotes or creation time. |
| `includeMedia` | boolean | Retain image and video links embedded in answer content. |
| `includeOutboundLinks` | boolean | Retain external links embedded in answer content. |

Example `input.json` (the Actor's published prefill):

```json
{
  "mode": "urls",
  "directUrls": [
    "https://www.quora.com/What-is-Python-primarily-used-for"
  ],
  "resultTypes": [
    "question",
    "answer"
  ],
  "includeAnswers": true,
  "maxAnswersPerQuestion": 2,
  "includeRelatedQuestions": false,
  "maxRelatedQuestions": 0,
  "includeAuthorProfiles": false,
  "maxAuthors": 20,
  "filterAiAnswers": "include",
  "answerSortOrder": "observed",
  "includeMedia": false,
  "includeOutboundLinks": false,
  "includeAnswerHtml": false,
  "includeProfileSensitiveFields": true,
  "includeSocialLinks": true,
  "maxItems": 3,
  "maxItemsPerSource": 0,
  "includeFieldEvidence": true,
  "onlyUnanswered": false
}
```

## Run

```bash
curl -sS -X POST "https://api.apify.com/v2/acts/vexaloom-labs~quora-keyword-search-and-author-discovery-scraper/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d @input.json > results.json
```

The call waits for the run to finish and returns the dataset items as a JSON array. If the run needs more than 300 seconds the API answers with HTTP 408; then start the run with `POST /v2/acts/vexaloom-labs~quora-keyword-search-and-author-discovery-scraper/runs`, poll `GET /v2/actor-runs/{runId}` until `SUCCEEDED`, and read `GET /v2/datasets/{defaultDatasetId}/items`.

## Output

Each item is one record. Published fields include: `content_type`, `title`, `url`, `question_id`, `answer_id`, `author_name`, `upvotes`, `view_count`, `comments_count`, `created_at`, `follower_count`, `answer_count`, `discovery_source`, `access_state`, `source_url`.

Summarise what was found, cite the record URLs when present, and say how many items came back. Do not invent values for empty fields.