Which social media data API is best for AI agents? Count the tokens a response takes (2026)

Why does the shape of a response matter so much to an AI?

Every token of a tool's answer goes into the AI's context and is paid for as input. A platform's raw JSON is made for its own app and pages: one video carries dozens of screen fields, the same image in several sizes, tracking parameters and nesting several levels deep. One page of Douyin search is 187k tokens raw, more than a model with a 128k context can hold.

At $3 a million input tokens, an AI reading one raw page of Douyin search spends $0.56; reading DataXAPI's page of the same results, $0.005. With the context saved, the AI reads more pages and does more in one conversation, and answers sooner.

The same page of data: how many tokens each?

Across 226 endpoints: 9.2M tokens of the platforms' raw JSON against 452k read by an AI through DataXAPI's MCP server, 95% fewer; the JSON of a direct API call is 961k, 90% fewer. The ones most used:

How it was counted: one recorded real response a endpoint (the one on its page). The raw JSON is the platform's answer as it came; DataXAPI's JSON is the default answer of a direct API call, meta and billing included; what an AI reads is the text the MCP server gives the model (the compact view, its first line included). Counted with GPT-4o's o200k tokenizer on 2026-11-06. A few endpoints that answer with a handful of numbers (read counts, transcripts) come out tens to hundreds of tokens larger, with the common envelope added.

What makes it clean?

Only the fields defined: each endpoint's page lists them one by one, and what the platform sends for its own screens (styling, tracking, the same image in several sizes) is left out.

One set of names on every platform: the author in author, likes, comments and plays in metrics, the time in published_at as ISO 8601 UTC, the next page in page.next_cursor. From Douyin to Xiaohongshu, the AI has no new structure to work out.

What the platform does not give is said: meta.unavailable lists the fields missing and why, so the AI does not read a missing value as 0.

Can it take fewer still?

Yes. Through MCP the compact view is already the default; on a direct API call view: "compact" takes that page of Douyin search's JSON to 1,678 tokens; fields keeps only the fields you name, such as ["text", "metrics.likes", "published_at"]; a list endpoint that takes limit returns fewer items. A result is kept for 24 hours and can be read again by its request ID, in another view, at no charge.

How do I connect it?

To connect: paste this to your AI assistant: “Read https://dataxapi.com/agents.md and set up DataXAPI here. My API key is dx_…; use it for every DataXAPI call.” A client that takes a remote MCP server can be given https://api.dataxapi.com/mcp with the header Authorization: Bearer <key> instead.

Why DataXAPI

• Steady: endpoints were available 99.9% of the last 30 days, on a public status page you can check.
• Fast: a paid call took 2.5 seconds at the median over the last 7 days (counted 2026-11-06).
• Fewer tokens: across 226 endpoints, one recorded real response each, the platform's own JSON comes to 9.2M tokens and what an AI reads through DataXAPI's MCP server to 452k, 95% fewer; the JSON of a direct API call is 90% fewer too (counted 2026-11-06). Every platform answers in one shape: the author in author, counts in metrics, the next page in page.next_cursor, so another platform needs no new parser.
• See before you pay: every endpoint's page shows a recorded real response, field by field.
• Spending you control: a failed call is not charged; each key can have a limit, X-Max-Price caps one call, and the same Idempotency-Key within 24 hours is never charged twice.
• Ready for AI: a remote MCP server listed in the official MCP registry as com.dataxapi/mcp, usable from Claude, Cursor and ChatGPT.
• Free trial of up to 333 calls, no card.

Endpoints

Each with its price; every page has the parameters, the fields and a recorded response.