---
layout: tap
site_name: tiktok
tap_name: trending
description: "TikTok Trending Videos (requires login or geo-access)"
intent: read
columns: []
args: []
args_json: |
  {}
health_json: |
  {"min_rows":3,"non_empty":["author"]}
example_args: ""
source_url: https://github.com/LeonTing1010/tap-skills/blob/main/community/tiktok/trending.plan.json
license: MIT
---

<section>
  <h2>What it does</h2>
  <p>TikTok Trending Videos (requires login or geo-access)</p>
</section>

<section>
  <h2>Install Taprun once</h2>
  <p>Taprun ships as a single MCP server exposing a catalog of compiled taps. One-time setup on macOS / Linux:</p>
  <pre><code>brew install LeonTing1010/tap/taprun
tap mcp connect</code></pre>
  <p>Or drop this into your <code>claude_desktop_config.json</code> (works identically in Claude Code, Cursor, Cline, Windsurf — any MCP host):</p>
  <pre><code>{
  &quot;mcpServers&quot;: {
    &quot;tap&quot;: {
      &quot;command&quot;: &quot;tap&quot;,
      &quot;args&quot;: [&quot;mcp&quot;, &quot;start&quot;]
    }
  }
}</code></pre>
</section>

<section>
  <h2>Call <code>tiktok/trending</code></h2>
  <p>Terminal, once installed:</p>
  <pre><code>tap run tiktok/trending</code></pre>
  <p>From the MCP host — exact same compiled plan, deterministic replay, zero LLM tokens:</p>
  <pre><code>tap.run({ site: &quot;tiktok&quot;, name: &quot;trending&quot; })</code></pre>
</section>

<section>
  <h2>Why compile it once</h2>
  <p>This plan was forged once — the AI read <code>tiktok</code>, picked stable structural addresses (JSON-LD, ARIA, RSS, or declared API endpoints, in that priority order), and saved them to a <code>.plan.json</code>. Every replay since then has used zero LLM tokens. When <code>tiktok</code> ships a site change that breaks the extraction, <code>tap verify</code> surfaces it before your data goes stale — not after your pipeline silently writes garbage for a week.</p>
</section>