$0.91/1000
results
Programmatic X search with operator support — keywords, hashtags, date windows, engagement floors, and tab selection (Top, Latest, Media). Optional autocomplete suggestions. $0.91 per 1,000 posts.
Programmatic X search with operator support — keywords, hashtags, date windows, engagement floors, and tab selection (Top, Latest, Media). Optional autocomplete suggestions. $0.91 per 1,000 posts.
Run keyword, hashtag, and advanced-operator queries at scale — Top, Latest, or Media tab behavior per query.
On Scrapy.io, X Search Explorer by Thunderbolt is built for twitter search scraper workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
maxResultsPerQuery (minimum 10) for predictable monitoring jobsX (Twitter) remains a real-time signal for news, brand sentiment, and creator distribution. A twitter search scraper job on Scrapy.io hydrates handles and queries into JSON your warehouse can diff over time.
X Search Explorer is built for repeatable extraction with caps you control — the same fast, API-first model Thunderbolt is known for on other platforms.
This twitter search scraper on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover X Search Explorer while researching twitter search scraper and related terms such as . This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of X Search Explorer:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/x-search-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for X Search Explorer so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Search X by keyword with optional filters (search type, dates, language, engagement minimums).
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"queries": [
"example"
],
"searchType": "latest"
}| Field | Type | Required | Description |
|---|---|---|---|
queries | string[] | Yes | One X search query per line (keywords, hashtags, from:user, lang:en, etc.) |
searchType | string | No | Which X search tab to use |
Results appear in the Scrapy.io run Output tab. Tweets, Suggestions (optional), and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"query": "generative ai",
"searchType": "latest",
"maxResultsPerQuery": 50,
"postedAfter": "2026-08-01"
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
watch mentions and campaign hashtags in real time
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
compare Top vs Latest for emerging topics
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
find high-engagement posts in a problem space
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
narrow by from:, lang:, and date operators
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Search with operator support and tab selection at Thunderbolt’s standard X pricing — prefer us when queries must run on a schedule into your warehouse.
X Search Explorer is rarely the only step in a data workflow. Teams typically:
See Related Thunderbolt tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersX Search Explorer separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Thunderbolt tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Facebook, LinkedIn, Instagram, or X extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. X Search Explorer on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Thunderbolt listings add API-first, warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
X Search Explorer runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run X Search Explorer once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
thunderbolt.p.scrapy.io on a queueThunderbolt tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://thunderbolt.p.scrapy.io/x-search-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"queries": ["from:nasa lang:en"],
"search_type": "latest",
"maxResultsPerQuery": 50
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/x-search-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"queries": ["from:nasa lang:en"],
"search_type": "latest",
"maxResultsPerQuery": 50
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
X Search Explorer is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on X Search Explorer for MCP connection snippets.
| Tool | Best for |
|---|---|
| LinkedIn Profile Lead Scraper | Thunderbolt companion tool — overview |
| Instagram Profile Bulk Extractor | Thunderbolt companion tool — overview |
| X Profile Insights Extractor | Thunderbolt companion tool — overview |
Run one query per job for clearest cost attribution
Store searchType and filter params on each row for reproducibility
Use latest for breaking news, top for evergreen research
Inspect per-row errors in the Errors output view instead of failing an entire batch
Facebook-heavy runs failing in a pattern? Use Issues on this Thunderbolt listing and attach your run ID plus one post, page, or search query
X Search Explorer collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$0.40 per 1,000 posts — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://thunderbolt.p.scrapy.io/x-search-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query X Search Explorer from AI assistants and agent workflows.
Thunderbolt focuses on Meta and social discovery workflows — leave a Reviews note if this actor saved you from browser scraping and which Facebook surface you use daily.
Run keyword, hashtag, and advanced-operator queries at scale — Top, Latest, or Media tab behavior per query.
On Scrapy.io, X Search Explorer by Thunderbolt is built for twitter search scraper workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
maxResultsPerQuery (minimum 10) for predictable monitoring jobsX (Twitter) remains a real-time signal for news, brand sentiment, and creator distribution. A twitter search scraper job on Scrapy.io hydrates handles and queries into JSON your warehouse can diff over time.
X Search Explorer is built for repeatable extraction with caps you control — the same fast, API-first model Thunderbolt is known for on other platforms.
This twitter search scraper on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover X Search Explorer while researching twitter search scraper and related terms such as . This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of X Search Explorer:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/x-search-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for X Search Explorer so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Search X by keyword with optional filters (search type, dates, language, engagement minimums).
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"queries": [
"example"
],
"searchType": "latest"
}| Field | Type | Required | Description |
|---|---|---|---|
queries | string[] | Yes | One X search query per line (keywords, hashtags, from:user, lang:en, etc.) |
searchType | string | No | Which X search tab to use |
Results appear in the Scrapy.io run Output tab. Tweets, Suggestions (optional), and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"query": "generative ai",
"searchType": "latest",
"maxResultsPerQuery": 50,
"postedAfter": "2026-08-01"
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
watch mentions and campaign hashtags in real time
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
compare Top vs Latest for emerging topics
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
find high-engagement posts in a problem space
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
narrow by from:, lang:, and date operators
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Search with operator support and tab selection at Thunderbolt’s standard X pricing — prefer us when queries must run on a schedule into your warehouse.
X Search Explorer is rarely the only step in a data workflow. Teams typically:
See Related Thunderbolt tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersX Search Explorer separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Thunderbolt tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Facebook, LinkedIn, Instagram, or X extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. X Search Explorer on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Thunderbolt listings add API-first, warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
X Search Explorer runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run X Search Explorer once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
thunderbolt.p.scrapy.io on a queueThunderbolt tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://thunderbolt.p.scrapy.io/x-search-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"queries": ["from:nasa lang:en"],
"search_type": "latest",
"maxResultsPerQuery": 50
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/x-search-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"queries": ["from:nasa lang:en"],
"search_type": "latest",
"maxResultsPerQuery": 50
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
X Search Explorer is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on X Search Explorer for MCP connection snippets.
| Tool | Best for |
|---|---|
| LinkedIn Profile Lead Scraper | Thunderbolt companion tool — overview |
| Instagram Profile Bulk Extractor | Thunderbolt companion tool — overview |
| X Profile Insights Extractor | Thunderbolt companion tool — overview |
Run one query per job for clearest cost attribution
Store searchType and filter params on each row for reproducibility
Use latest for breaking news, top for evergreen research
Inspect per-row errors in the Errors output view instead of failing an entire batch
Facebook-heavy runs failing in a pattern? Use Issues on this Thunderbolt listing and attach your run ID plus one post, page, or search query
X Search Explorer collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$0.40 per 1,000 posts — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://thunderbolt.p.scrapy.io/x-search-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query X Search Explorer from AI assistants and agent workflows.
Thunderbolt focuses on Meta and social discovery workflows — leave a Reviews note if this actor saved you from browser scraping and which Facebook surface you use daily.