SocialCrawl

AI Agent Integration

Using SocialCrawl with AI agents and LLMs

AI Agent Integration

SocialCrawl is designed to be consumed by AI agents. Every response includes computed fields (engagement_rate, language, content_category, estimated_reach) that are ready for downstream reasoning, and the entire 108-endpoint catalogue is published as machine-readable llms.txt and OpenAPI files.

If you're using Claude Code, Cursor, Windsurf, Claude Desktop, or any other MCP-compatible client, install the Skills or MCP package for a one-line setup — your agent gets local schema validation, zero-credit discovery tools, and direct access to every endpoint without plumbing any HTTP yourself.

How do AI agents discover SocialCrawl?

We provide machine-readable documentation at:

  • /llms.txt — Compact overview of the API
  • /llms-full.txt — Complete self-contained reference, including the GET /v1/credits/balance meta endpoint, idempotency semantics, oneOf parameter constraints, and all error codes

Point your AI agent at /llms-full.txt for everything it needs to use the API.

How can agents use the OpenAPI specification?

The full OpenAPI 3.1 specification is available at:

What per-platform documentation is available?

Fetch only the endpoints you need — one file per platform:

Each file contains only that platform's endpoints with parameters, descriptions, and curl examples.

How do I integrate SocialCrawl with my AI agent?

const response = await fetch(
  "https://socialcrawl.dev/v1/tiktok/profile?handle=charlidamelio",
  { headers: { "x-api-key": process.env.SOCIALCRAWL_API_KEY } },
);
const { data } = await response.json();
// data is structured and ready for AI reasoning

Official AI Framework Documentation

AI Agent Integration | Socialcrawl