The social data layer for AI agents

Why agents like this API

• Transcripts and AI summaries are first-class endpoints, not an afterthought — the exact inputs an LLM wants.
• Consistent fields across platforms: one tool definition covers five sites.
• No HTML soup. Every response is compact JSON with predictable keys, so you spend tokens on content instead of markup.
• MCP server at mcp.scrapersocial.com exposes every endpoint as a tool for Claude and any MCP client.

A research agent in one prompt

Connect the MCP server, then ask in plain language. The agent chains get_channel_posts and get_transcript on its own:

> pull the last 10 reels from @thefoodlab,
  transcribe them, and rank by views

tool: get_channel_posts(handle="thefoodlab", platform="instagram")
tool: get_transcript(url=…) x 10
result: ranked table with views, likes, and a one-line
        summary per reel

Or wire it up as a function tool

Any framework that speaks function calling can wrap an endpoint in a few lines:

const tools = [{
  name: "get_tiktok_transcript",
  description: "Transcript of a public TikTok video",
  input_schema: { type: "object", properties: { url: { type: "string" } } },
  run: async ({ url }) =>
    (await fetch(`https://api.scrapersocial.com/v1/tiktok/transcript?url=${url}`, {
      headers: { Authorization: `Bearer ${process.env.SCRAPERSOCIAL_KEY}` },
    })).json(),
}];

Full setup guides for Claude, Claude Code, Cursor, and ChatGPT live on the MCP page, and agent-flavored docs at /ai-agents.