investor-agent

Overview

The investor‑agent MCP server fills a critical gap for AI assistants that need reliable, up‑to‑date financial intelligence. By exposing a rich set of market data and analytical tools, it allows developers to augment conversational agents with real‑time insights into stocks, options, earnings, and market sentiment. Rather than forcing users to scrape multiple websites or manage complex API keys, the server presents a unified interface that returns clean, well‑structured JSON objects ready for downstream processing.

The core value lies in its breadth of data sources and the depth of analysis. From a single request you can retrieve market movers, detailed ticker reports (including news and analyst recommendations), filtered options chains, historical price trends, full financial statements, institutional ownership, insider activity, and upcoming earnings. The server also offers sentiment metrics such as the CNN Fear & Greed Index or Google Trends data, giving AI agents a holistic view of both fundamentals and market psychology. This breadth makes it possible to build sophisticated financial advisory bots, automated trading assistants, or research tools that can respond instantly to user queries about any public company.

Key capabilities are delivered through a set of well‑named tools. Each tool accepts intuitive parameters—such as the number of top movers, date ranges for options, or the frequency of financial statements—and returns data that is automatically cached and rate‑limited to respect external APIs. The architecture layers caching (via ’s smart cache, , and ) to ensure fast responses even under heavy load. When technical indicators are needed, optional TA‑Lib integration provides SMA, EMA, RSI, MACD, and Bollinger Bands without requiring the developer to implement them from scratch.

In practice, developers can integrate investor‑agent into a Claude or GPT‑based workflow by simply adding the MCP endpoint to their tool list. For example, a financial advisor chatbot can ask for “the top 10 gainers in the S&P 500 today” and immediately receive a structured list, or it can request “historical earnings for Apple over the last four quarters” and get a ready‑to‑display table. The server’s clean API also makes it ideal for batch analyses, such as scanning a portfolio of tickers to surface undervalued stocks or monitoring insider buying activity for potential signals.

What sets investor‑agent apart is its commitment to performance and reliability. By combining multiple caching layers with automatic interval selection for price history, it keeps data payloads small while maintaining accuracy. The optional TA‑Lib support gives developers access to industry‑standard technical analysis without the overhead of maintaining their own indicator library. Together, these features enable developers to build AI assistants that provide fast, trustworthy financial insights at scale—an essential capability for any application where market timing or investment decisions matter.