Build guide

Build a viral TikTok research pipeline in 10 minutes

If you're researching TikTok for a brand, a client, or your own channel, the workflow is the same: pull a creator's recent winners, surface the trends in their niche, and grab the proven viral templates you can adapt. This guide chains three Hooklayer MCP tools so Claude Desktop runs the whole pipeline from one chat — no scripts, no scrapers, no manual copy-paste.

Who this is for: Indie developers and content founders who want creator intel without writing scrapers. Works with Claude Desktop, Claude.ai, Cursor, n8n, or any MCP client.

Tools used in this chain

analyze_account

Pulls viral DNA scores, format fingerprint, steal map for any TikTok or YouTube creator. The starting point for every research thread.

trend_pulse

Live niche trend intel — what's rising and saturated this week in the creator's niche. Niche-native sources (Google News, YouTube velocity, niche subreddits).

find_viral_template

Ranked viral templates in the niche with example video URLs you can study or remix.

Steps

1
Connect Hooklayer to Claude Desktop
Open your claude_desktop_config.json (macOS: ~/Library/Application Support/Claude, Windows: %APPDATA%\Claude) and add Hooklayer as an MCP server with your free API key.
```
{
  "mcpServers": {
    "hooklayer": {
      "url": "https://hooklayer.dev/api/mcp",
      "transport": "http",
      "headers": { "Authorization": "Bearer hl_live_..." }
    }
  }
}
```
2
Ask Claude to analyze the creator
In Claude Desktop, type: "Use Hooklayer to analyze @creatorhandle on TikTok and show me the viral DNA + steal_map." Claude calls analyze_account and returns viral_dna_score, replicability_score, format fingerprint, top 5 videos with transcripts, and a recommended_chain field naming the next 3 tool calls to make.
3
Let Claude chain into trend_pulse for the creator's niche
analyze_account returns the creator's niche in profile.niche. Claude reads recommended_chain and fires trend_pulse on that niche — you get rising opportunities + saturated patterns + per-trend signal_strength scores. Provenance includes data_sources so you can audit every claim.
4
Pull viral templates with find_viral_template
Final step in the chain: find_viral_template on the same niche returns 5-10 ranked templates with hook_pattern, format, avg_views, and example URLs. Each URL can be piped straight into viral_remix later if you want to draft fresh scripts from the same skeleton.
5
Audit + ship
Every response carries a quality block (level: full | partial | degraded, plus reason) and provenance (sources, fetched_at, data_age_days, rubric_version). Cite quality verbatim in any output you hand to a client.

Why this approach

Most "viral research" workflows are manual: open TikTok, scroll, screenshot, take notes. This is one Claude chat that takes 30 seconds and surfaces auditable scoring.
The recommended_chain field is the agentic anchor — analyze_account doesn't just return data, it tells the agent which tool to call next with the parameters pre-filled. Claude doesn't need prompt engineering, it reads the chain.
Quality flags make the workflow client-safe. If trend_pulse only finds 1 rising trend, the response carries `quality.level: partial` and the reason. You never silently hand off "thin" data as full intelligence.