TikTok Comments From Post

Give it a TikTok video URL, get back the comments and the profiles that left them.

What it does

• Collects comments and commenter profiles from one TikTok video.
• Useful for building warm lead lists from an engaged audience: people who commented on a video about your category.
• Returns TikTok profiles. There is no email and no job title in the response.

This gives you commenters. Any contact detail has to come from a separate enrichment step — match the person on LinkedIn with find_people, then run find_work_email.

Use cases

• Turn an engaged video audience into a list. People who commented on a video in your category have shown interest in public.
• Read what an audience actually asks. Comment themes surface the objections and questions your copy should answer.
• Find creators and buyers in one pass. Active commenters under category content are often practitioners worth following up.
• Chain it off a topic search. Run tiktok_search_query first, then pull comments from whichever video landed.

How to use it

Search TikTok for videos about "sales automation" and show me the ones with
the most comments.

Collect the comments from that video. 100 results.

Group the comments by theme. What problem do these people keep describing?

Keep the commenters whose profile names a business or a role. Drop the rest.

Example prompts

Best prompting practices

• Raise max_results. The default of 10 is a sample, not a list. 100 is the ceiling — and since billing is per comment, that ceiling is what a full call costs.
• Use it for research before outreach. TikTok comments are strong for language and objections, weaker for identity — the words are often more valuable than the names.
• Ask for the objections, grouped. “Group the comments into questions, objections and praise, and quote three of each” is a usable output; 100 raw comments in the chat is not.
• Keep the commenter handle beside the quote so a line worth following up on can be traced back to an account.
• Chain a search first. tiktok_search_query finds the videos worth reading; this tool reads them.
• Do not expect firmographics. No company, no title, no email. Plan the enrichment step separately.
• Number the steps across a video list. Search → pick the videos → pull comments → summarise across all of them. One prompt per step keeps the per-comment bill visible.
• Cap the batch. “Pull comments from these 20 videos, then stop and report credits used.”

Output

{
  "comments": [
    {
      "username": "janedoe",
      "text": "We spent months on this and still export to CSV.",
      "likes": 42,
      "profile_url": "https://tiktok.com/@janedoe"
    }
  ]
}

Credits and limits

0.3 credits per comment returned. max_results accepts 1–100 and defaults to 10, so a default call costs up to 3 credits and a full call up to 30. Maximum 100 comments per call. Run check_credits (free) before working through a list of videos.

Parameters

Related tools

Next steps

• Automated Outbound — build a warm list from post engagement, end to end
• All MCP tools — the full catalogue with credit costs

Keep learning

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