Technical Analysis

The Technical Analysis MCP server gives AI agents direct, programmatic access to over 150 TA-Lib indicators across stocks, cryptocurrencies, and Indian equity markets (NSE/BSE). Instead of building a custom quant pipeline for every model you use, you connect this technical analysis MCP server once through BusinessMCP and expose the same indicator library, screening logic, and signal outputs to Claude, GPT, Gemini, or any other model-agnostic agent via a single hosted /api/mcp endpoint secured with a Bearer mcph_* key. This makes it a natural fit for teams that want AI-powered technical analysis for stocks and crypto without maintaining separate integrations per model or per market.

Under the hood, the server wraps TA-Lib's full indicator set — moving averages, oscillators, momentum, volatility, and volume-based indicators — so an agent can request anything from RSI and MACD to Bollinger Bands or ADX for a given symbol, timeframe, or exchange. It also supports multi-symbol stock screening, letting an agent scan a watchlist or index constituents for combinations of technical conditions (e.g., oversold RSI plus bullish MACD crossover) rather than pulling raw price data and computing indicators client-side. Coverage of Indian markets alongside major crypto and equity venues means the same MCP server can serve a trading desk that spans NSE-listed stocks and BTC/ETH pairs without swapping tools mid-workflow.

Because this technical analysis MCP server sits inside BusinessMCP's unified hosting layer, it doesn't operate in isolation. Requests, indicator usage, and screening activity roll up into the same business-intelligence dashboard used for your other connected tools, databases, and ad platforms, so a finance or product team can see how often agents pull technical signals, which symbols get screened most, and how that activity correlates with other revenue or engagement data — all cookieless and GDPR-friendly. Whether it's called from your own cloud growth-suite app or invoked directly by an external agent hitting your /api/mcp endpoint, the interface and auth model stay identical.

This makes the server well suited to fintech products building AI research assistants, trading bots that need on-demand indicator computation, brokerage or newsletter tools generating automated technical commentary, and internal analyst copilots that need to screen large watchlists quickly. Teams already using BusinessMCP for other data sources can add crypto technical analysis and Indian stock market screening as another capability in the same hosted MCP server stack, rather than standing up a separate service, credential set, and monitoring pipeline just for TA-Lib indicators.

For developers, the practical benefit is fewer moving parts: one authenticated endpoint returns structured indicator values, screened symbol lists, and signal summaries that any LLM can reason over directly, instead of raw OHLCV arrays that need post-processing. That reduces token overhead for the calling agent and keeps technical-analysis logic centralized, versioned, and auditable through BusinessMCP's dashboard rather than scattered across client-side scripts.