aTars is expanding its MCP server infrastructure to cover more crypto assets and signal types. The platform pre-computes 40+ technical indicators on 1-minute candle updates, eliminating calculation lag when AI agents query data.
Technical architecture:
- Continuous ingestion of 1-min OHLCV candles with hourly resampling
- Pre-computed indicators using TA-Lib and custom engines
- Perplexity AI integration for daily news sentiment
- MCP endpoints exposing structured tools to Claude
The approach is domain-agnostic - any real-time data pipeline can follow this pattern: pre-compute signals, expose typed MCP endpoints, and let AI agents decide which tools to call.
The team emphasizes that the challenge isn't building the MCP server itself, but maintaining the data pipeline behind it. Currently supports 9 major tokens (BTC, ETH, SOL, XRP) with 90-day rolling windows.
The data pipeline -- under the hood: The backend continuously ingests 1-min OHLCV candles, resamples to hourly, and pre-computes 40+ indicators on every new candle close. By the time Claude asks -- the answer is already computed. No on-demand calculation lag. The stack: →
The pattern generalises beyond crypto. Any domain with a real-time data pipeline can do this: → Pre-compute your derived signals on every update → Expose them as typed MCP tool endpoints → Let Claude decide which tools to call → Get grounded, reasoned, visualised output
Most crypto AI teams spend weeks building the same data pipeline before writing a single line of model code. Connect to exchange. Normalize candles. Compute RSI, MACD, Bollinger. Handle gaps. Repeat for every token. We got tired of watching this happen. So we built the
Most crypto ML teams spend more time on their data pipeline than on their models. Teams spend weeks on: connect to an exchange, normalize OHLCV, compute indicators, handle gaps, resample, repeat. Before a single model trains, you've already burned engineering time on
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