LinkedIn Profile Reactions

Give it a LinkedIn profile URL, get back the posts that person has reacted to.

What it does

• Returns the posts a profile has reacted to — liked, celebrated or reposted. A bare username works in place of the full URL.
• Each result carries the post’s author, content, engagement counts and URL.
• Useful for spotting what a prospect engages with before you reach out.
• posted_limit narrows results by recency — any, 24h, week, month, 3months, 6months.

Direction matters here. This tool answers “what did this person react to”. To answer “who reacted to this post”, use linkedin_post_engagers instead — it takes a post URL and returns the accounts that engaged with it.

Use cases

• Read interest before there is intent. What someone quietly likes shows the topics they follow without having to post about them.
• Map influence around a buyer. Consistent reactions to one author reveal who the prospect takes cues from.
• Find a timely hook. A post they engaged with this week is a natural, non-creepy opener.
• Fill in a thin profile. For prospects who neither post nor comment, reactions may be the only activity signal available.

How to use it

Show me what linkedin.com/in/janedoe has reacted to in the past month.

Get 25 posts she reacted to over the last 3 months. Group them by the
author of the post.

Pick the reaction that is most relevant to revenue operations and draft
a two-line opener referencing the idea in that post — not the fact that
she liked it.

Example prompts

Best prompting practices

• Say “what they reacted to”. Phrasing it as “who reacted” sends your client to the post-engagement tool, which answers the opposite question.
• Set posted_limit. A reaction from six months ago is not a timing signal. week or month is usually what you want for outreach.
• Widen before you conclude anything. 24h will often return nothing — most people do not react to something every day. Retry at week or month before calling the profile inactive.
• Raise max_results for pattern-finding, but raise it deliberately. The default of 3 is a spot check; 25 shows a pattern — and costs eight times as much, since you pay per reaction.
• Ask for the post behind the reaction. Author, post topic, date and URL — the reaction on its own is a click, and the post is the thing you can actually talk about.
• Ask for the pattern across the reactions. “Which topics and which authors repeat” is a read on what they care about; a list of 25 individual likes is not.
• Reference the idea, not the click. “I saw you liked a post” reads as surveillance. Referencing the argument in that post reads as relevance.
• Pair it with comments. Reactions show interest; linkedin_profile_comments shows opinion. Both are 0.2 credits per record returned.

Output

{
  "reactions": [
    {
      "post_author": "John Smith",
      "content": "Most RevOps teams measure the wrong thing...",
      "engagement": { "reactions": 412, "comments": 58 },
      "url": "https://linkedin.com/posts/johnsmith-activity-456"
    }
  ]
}

Credits and limits

0.2 credits per reaction returned. max_results accepts 1–50 and defaults to 3, so the default call costs up to 0.6 credits and a 50-result call up to 10. A profile with fewer reactions than the cap costs less. Run check_credits (free) before a large batch.

Parameters

Related tools

Next steps

• All MCP tools — the full catalogue with credit costs
• MCP use cases — personalisation workflows
• AI for Sales Reps — workflows that chain this tool into outreach

Keep learning

Two courses take these tools past the reference page — clustered by GTM job, then chained into workflows.