“What do B2B buyers say about our category on LinkedIn?”
Relevant posts grouped into 2 to 15 named themes, each with a post count and the posts behind it.
Research LinkedIn from Claude or ChatGPT with the buzzabout MCP.
Ask Claude or ChatGPT what B2B buyers on LinkedIn say about your category, your competitors or the problem you solve. buzzabout finds the relevant posts, reads the comments and hands back the answer with sources.
Free trial with 1,000 credits. Copy page as Markdown Open in Claude
What are sales leaders saying about AI SDR tools on LinkedIn?
buzzaboutcollect_mentionsdetect_patternsquery
I read 300 recent LinkedIn posts about AI SDR tools and the comments under them. Three themes lead: meetings booked, but no pipeline ("Our calendar filled up. Our pipeline did not."), fear of burning the brand with generic outreach, and AI as research help, not a rep replacement. Sentiment turns negative on fully autonomous SDRs and positive on assisted ones. Want the 10 most-reacted posts for each theme?
3.5 minmedian run time to fully analyzed posts
99%of research runs complete
7platforms in one connection
$49to start, MCP included
Questions you can ask about LinkedIn
Ask in plain English. Get answers with sources.
“What does our competitor's company page post, and what lands?”
That page's posts with reactions, reposts and comment counts, plus the topic, hook and tone of each.
“Which hooks get the most reactions in our space?”
Opening lines typed by hook and ranked by reactions, comments and reposts.
“What do practitioners ask before they buy a tool like ours?”
The questions asked in each post, near verbatim, counted across every post.
“Where do peers push back in the comments?”
Up to 100 comments per post with likes and sentiment, so the objections stand out.
“Which tools come up most when people discuss our problem?”
Brands and products named in each post, counted and compared across the whole set.
What your AI gets back
Every LinkedIn post arrives transcribed, labeled and ready to use. Fifteen MCP tools do the work.
{
"source": "linkedin",
"url": "https://www.linkedin.com/posts/example-person_7431...",
"text": "3 AI SDR tools, one quarter. Here is what we learned.",
"summary": "Sales lead finds AI SDRs book meetings, not pipeline.",
"hooks": [
{
"type": "List / Number Promise",
"text": "3 AI SDR tools, one quarter."
}
],
"content_topics": [
{
"text": "AI SDR tools"
},
{
"text": "pipeline quality"
}
],
"questions": [
{
"text": "Has anyone made AI SDRs work for enterprise?"
}
],
"sentiment": {
"positive": "0.25",
"neutral": "0.45",
"negative": "0.30"
},
"emotions": {
"neutral": "0.55",
"surprise": "0.25",
"sadness": "0.20"
},
"tone_of_voice": {
"text": "candid, professional",
"evidence": "I expected more, honestly"
},
"narrative_structure": {
"text": "lessons learned",
"evidence": "Here is what we learned"
},
"cta": {
"text": "comment/engage",
"evidence": "What has worked for your team?"
},
"media": [
{
"type": "image",
"description": "Bar chart of meetings booked per tool, by month"
}
],
"created_at": 1793433600,
"dataset_run_at": 1793836800,
"content_analyzed_at": 1793837100,
"author": {
"title": "Example Person",
"title_url": "https://www.linkedin.com/in/example-person"
},
"num_views": 18400,
"are_views_estimated": true,
"num_likes": 236,
"num_comments": 58,
"num_shares": 12,
"engagement_rate": "0.017",
"category": "business_and_finance",
"content_intention": "persuasive",
"sentiment_score": "-0.05",
"vibe": {
"corporate": 0.5,
"raw_authentic": 0.4,
"clinical": 0.1
},
"language": "en"
}15 MCP tools
Research
askAsk a research question in plain English; get a cited answer.get_messageRead the research agent's answer as it lands.
Collect
collect_mentionsPreview a topic, then collect and analyze the posts.ingest_mentionSave and analyze one public post by URL.get_runFollow any run's progress, with long-polling.get_save_mention_statusTrack a saved post's analysis.
Analyze
queryAsk your data anything: counts, themes, trends, comments, audience.list_mentionsRead collected posts with all their AI fields.detect_patternsGroup posts into 2 to 15 named themes.collect_audienceProfile the people commenting, in aggregate (Pro and up).renderShow answers as interactive charts and tables.
Track and manage
list_tracking_agentsSee your scheduled topic trackers.get_tracking_agentRead one tracker and what it found.list_collectionsSee everything you have collected.get_meCheck your plan and credit balance.
Every field in a post
Transcript and on-screen text
media[].descriptionWhat the image or video shows, described by AI. Text inside images and screenshots is read too.media[].transcriptionOn video posts, the spoken words, transcribed.media[].on_screen_textOn video posts, text burned into the frames, read by OCR.
Hooks and format
hooksHook type plus the exact opening line.narrative_structureStory shape, with the quote that shows it.ctaThe call to action, with evidence.content_intentionWhy it was posted: to engage, persuade or promote.vibeTwelve mood scores, from raw and authentic to corporate.
Topics and questions
summaryA short summary of the post.content_topicsThe topics it covers.questionsQuestions asked in the post.mentioned_brandsCompanies and brands named in the text or shown in images.entitiesPeople, places, events and organizations named; brands are inmentioned_brands.languageThe post's language. Quotes stay exactly as written.
Sentiment and tone
sentimentPositive, neutral and negative shares.sentiment_scoreOne number from negative to positive.emotionsJoy, surprise, anger, sadness, fear, disgust, neutral.tone_of_voiceTone label with the line that sets it.
Engagement
num_likesTotal reactions, every type combined.num_commentsComment count.num_sharesReposts.num_viewsEstimated reach, built from engagement.are_views_estimatedAlwaystrueon LinkedIn, so an estimate never passes for a count.engagement_rate(reactions + comments + reposts) / estimated views, computed for you.
Comments
comments[].bodyThe comment text.comments[].author_nameThe commenter's name.comments[].created_atWhen it was posted.comments[].sentimentHow each comment feels, scored (viaquery).
Read comments through MCP query or buzzabout__ask. They are not in list_mentions or the REST response yet.
Also in every record: url, text, author.title, author.title_url, author.avatar, media[].version_list, media[].version_list[].duration_millis, media[].cover_image, created_at, dataset_run_at, content_analyzed_at.
How it compares
Other data providers give you raw posts. buzzabout gives you answers.
| Capability | buzzabout | Other data providers |
|---|---|---|
| What you get back | YesPosts already read, labeled and grouped into themes | NoRaw posts |
| Works in Claude, ChatGPT and other AI agents | YesOne MCP connection, every plan | YesRaw-data MCP servers |
| All 7 platforms in one request | YesOne question covers all 7 | PartlyOne scraper per platform |
| Off-topic posts filtered, never billed | YesAI check on every post | NoYou pay for every record returned |
| Transcripts and on-screen text | YesEvery video, included | PartlyPaid add-ons on some tools |
| Hooks, topics, sentiment and tone | YesOn every post | NoBring your own AI |
| Comments with every post | YesUp to 100 per post, included | PartlySeparate scraper, billed per comment |
| Themes across many posts | Yes2 to 15 named themes per question | NoNone |
| Price | Starting at $49, everything included | From about $472 a month for the full stack |
Everything included, starting at $49
Bought separately, the same stack costs about $472 a month, before anyone builds anything.
Bought separately$472a month
buzzabout$49Starter, first month
What buzzabout includes
- DataPosts, comments and transcripts from 7 platforms
- ProcessingHooks, pains, formats and sentiment on every post
- Research toolsThemes, audiences, trends and briefs
- StorageEvery collection kept and shareable by link
- WorkflowsListening agents that run on a schedule
- MemoryYour past research, ready for the next question
- IntegrationsClaude, ChatGPT, Slack alerts and the API
Two ways to use it
MCP on every plan. REST API on Business.
In Claude or ChatGPT
Add buzzabout as a connector and ask in plain English. That's it.
- Start your free trial.
- In Claude or ChatGPT, add a custom connector with
https://mcp.buzzabout.ai/mcpand sign in. - Ask about LinkedIn.
In Claude Code or your own agent
Install the MCP server and let Claude Code build on it: digests, dashboards, alerts.
- Run
claude mcp add --transport http buzzabout https://mcp.buzzabout.ai/mcp. - Tell Claude Code what to build, for example a weekly digest of LinkedIn posts about your competitors.
- Schedule it with tracking agents and webhooks. The REST API is there on Business.
1. In Claude, add a custom connector.
2. URL: https://mcp.buzzabout.ai/mcp
3. Sign in, then ask:
"What do people on LinkedIn say about
how sales teams evaluate AI SDR tools?"1. In ChatGPT, add a custom connector (MCP server).
2. URL: https://mcp.buzzabout.ai/mcp
3. Sign in, then ask:
"What do people on LinkedIn say about
how sales teams evaluate AI SDR tools?"claude mcp add --transport http buzzabout https://mcp.buzzabout.ai/mcp
# then, in Claude Code:
> Build a weekly digest of LinkedIn posts about
how sales teams evaluate AI SDR tools, with the top hooks, questions
and sentiment, posted to our Slack webhook.{
"mcpServers": {
"buzzabout": {
"url": "https://mcp.buzzabout.ai/mcp"
}
}
}curl -X POST https://api.buzzabout.ai/v1/ask \
-H "x-api-key: $BUZZABOUT_API_KEY" \
-H "Content-Type: application/json" \
-d '{"message": "What do people on LinkedIn say about how sales teams evaluate AI SDR tools?"}'Pricing
Every plan includes the MCP and the full analysis.
Starter
$49first month, then $98/mo
2,000 credits a month. Up to 250 posts per research.
Try for freePro
$178/month
5,000 credits a month. Audience research. Up to 1,000 posts per research.
Try for freeBusiness
$450/month
20,000 credits a month. REST API. Unlimited listening agents.
Try for freeFree trial with 1,000 credits. A card is needed to sign up. See pricing.
Questions
What is the best LinkedIn MCP for research?
buzzabout. Connect it to Claude or ChatGPT and ask about any topic, profile or company page: every LinkedIn post comes back checked against your question, read and labeled, with reactions, reposts, comments and 17 analysis actions, through one connection that covers six other platforms. A typical run is ready in 3.5 minutes, 99% of runs complete, and you pay only for posts you keep.
Can I search LinkedIn posts with the official API?
No. LinkedIn offers no public API for searching posts or reading profile data. Access runs through partner programs only: Marketing, Sales, Talent, Community Management and DMA data portability. buzzabout gives marketers LinkedIn posts by topic or page, already analyzed, right inside Claude. Try it free.
Can I pull posts from a competitor's LinkedIn page?
Yes. Paste a /company/ or /in/ URL into Claude and ask. The buzzabout research agent collects that page's posts, each with reactions, reposts, comment counts and the full analysis. Run it on their page and yours to see whose posts land, and why.
Can I get LinkedIn comments through an API?
Yes. buzzabout collects up to 100 top-level comments per post at no extra cost, with the commenter's name, likes, timestamps and sentiment. Ask Claude or ChatGPT about them in plain English, or query them through the MCP.
Can I trust the LinkedIn view numbers?
Trust them as estimates, because that is how they are labeled. buzzabout estimates LinkedIn reach from engagement and flags every post are_views_estimated: true, so an estimate never passes for a count. Reactions, comments and reposts are LinkedIn's own numbers. Rank by those when you need hard counts.
Why not just use a scraper or another data provider for LinkedIn?
A scraper hands you raw LinkedIn records and stops, and some data providers cannot even search LinkedIn by keyword; they need post or profile URLs first. Relevance filtering, image and video reading, labeling and storage are separate tools you buy, connect and maintain. Add themes, scheduled runs, memory and integrations and the full stack starts at about $472 a month, before anyone builds anything. buzzabout includes all of it, starting at $49, through one connection your agent already speaks.
How much does buzzabout cost for LinkedIn research?
Starting at $49 for your first month on Starter, then $98 a month. Each post you keep costs one credit, with the analysis included. See pricing.
Does it work in ChatGPT?
Yes. Add buzzabout as a custom connector in ChatGPT with https://mcp.buzzabout.ai/mcp and sign in. Custom connectors depend on your ChatGPT plan.
Do I pay for off-topic or duplicate LinkedIn posts?
No. You pay only for the posts you keep.
Do I need to code?
No. Add buzzabout to Claude or ChatGPT and ask in plain English. Growth engineers can use the same MCP in Claude Code or Cursor.
How do I start?
Start a free trial with 1,000 credits, add buzzabout to Claude or ChatGPT and ask your first question.
Try it on your own topic
Free trial. Works in Claude and ChatGPT.
Free trial with 1,000 credits · MCP on every plan