Quora Question, Answer & Profile Search Scraper: MCP config, n8n workflow and API

{
  "mode": "urls",
  "searchTarget": "questions",
  "directUrls": [
    "https://www.quora.com/What-is-Python-primarily-used-for"
  ],
  "resultTypes": [
    "question",
    "answer"
  ],
  "includeAnswers": true,
  "maxAnswersPerQuestion": 2,
  "expandRelatedQuestions": false,
  "maxRelatedQuestionsPerSeed": 0,
  "maxQuestionsPerQuery": 1,
  "includeAuthorProfiles": true,
  "maxAuthors": 1,
  "minAnswerUpvotes": 0,
  "filterAiAnswers": "include",
  "answerSortOrder": "observed",
  "onlyUnanswered": false,
  "includeProfileSensitiveFields": true,
  "includeSocialLinks": true,
  "includeAnswerHtml": false,
  "maxItems": 3,
  "includeFieldEvidence": true
}

---
name: quora-question-answer-and-profile-search-scraper
description: "Quora questions, full answers, profiles, authors, topics, search-result cards, access-status rows, and a run manifest. Runs the Apify Actor dataspryva/quora-question-answer-and-profile-search-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: "dataspryva/quora-question-answer-and-profile-search-scraper"
  store-url: "https://apify.com/dataspryva/quora-question-answer-and-profile-search-scraper"
---

# Quora Question, Answer & Profile Search Scraper

Use this skill to fetch Quora data with the Apify Actor `dataspryva/quora-question-answer-and-profile-search-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 `search`, `urls`, `authors` | Search discovers questions through the DuckDuckGo index and Quora related questions. URLs is deterministic and does not depend on search. |
| `searchQueries` | array | Two to four word phrases work best. Each query finds at most a couple of question seeds, then related questions expand the result. |
| `searchTarget` | one of `questions`, `profiles`, `both` | Choose whether keyword discovery looks for question pages, profile pages, or both. Profile search is best-effort because search engines index few Quora profiles. |
| `directUrls` | array | Question, answer, profile, or topic URLs. Language subdomains are accepted. Comma-separated or one URL per line. |
| `usernames` | array | Quora profile usernames or slugs, with or without @ or a trailing slash. Each is normalised to a profile URL. |
| `resultTypes` | array | Which rows to save. Status and run-manifest rows are always emitted so blocked or empty sources stay visible. |
| `includeAnswers` | boolean | Expand question pages into answer rows. Turn off to keep question rows only. |
| `maxAnswersPerQuestion` | integer | Answers to collect from each question, including the 3-5 already visible on the page. Zero means no answers. |
| `expandRelatedQuestions` | boolean | Open related questions linked from each question page. Quora exposes up to 70, which is how keyword search reaches more than its first result. |
| `maxRelatedQuestionsPerSeed` | integer | How many related questions to open from each question page. Zero lists the links without opening them. |
| `maxQuestionsPerQuery` | integer | Hard cap on question rows for each search query, including seed and related questions. |
| `includeAuthorProfiles` | boolean | Open each discovered author's profile to add follower, answer, question, view, and join-date fields. Turn off to keep author rows from answer evidence only. |
| `maxAuthors` | integer | Maximum unique author rows to emit when author is included in Result Types. |
| `minAnswerUpvotes` | integer | Keep only answers with an explicit upvote count at or above this value. Answers with unknown upvotes are excluded when this is positive. |

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

```json
{
  "mode": "urls",
  "searchTarget": "questions",
  "directUrls": [
    "https://www.quora.com/What-is-Python-primarily-used-for"
  ],
  "resultTypes": [
    "question",
    "answer"
  ],
  "includeAnswers": true,
  "maxAnswersPerQuestion": 2,
  "expandRelatedQuestions": false,
  "maxRelatedQuestionsPerSeed": 0,
  "maxQuestionsPerQuery": 1,
  "includeAuthorProfiles": true,
  "maxAuthors": 1,
  "minAnswerUpvotes": 0,
  "filterAiAnswers": "include",
  "answerSortOrder": "observed",
  "onlyUnanswered": false,
  "includeProfileSensitiveFields": true,
  "includeSocialLinks": true,
  "includeAnswerHtml": false,
  "maxItems": 3,
  "includeFieldEvidence": true
}
```

## Run

```bash
curl -sS -X POST "https://api.apify.com/v2/acts/dataspryva~quora-question-answer-and-profile-search-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/dataspryva~quora-question-answer-and-profile-search-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`, `search_rank`, `search_result_type`, `discovery_source`, `source_query`, `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.