Yahoo Finance MCP Server

The Yahoo Finance MCP server bridges AI assistants with real‑time and historical financial data sourced from Yahoo’s extensive market database. It solves the common developer pain point of building a custom finance API: instead of parsing HTML, managing rate limits, or stitching together multiple services, the server exposes a single, well‑documented set of tools that return structured JSON for stock tickers, news, searches, top performers, price history, options chains, and earnings. This allows AI assistants to answer complex financial queries—such as “Show me the top‑performing tech companies this quarter” or “What are Apple’s upcoming earnings dates?”—without any additional backend logic.

The server’s value lies in its asynchronous, non‑blocking design. All endpoints are implemented with , ensuring that an AI assistant can make multiple concurrent calls (e.g., fetching ticker data and related news simultaneously) without stalling the conversation. Proxy support further protects against throttling by allowing requests to be routed through HTTP/HTTPS or SOCKS proxies, a common requirement for production deployments. The protocol follows standard MCP conventions, so any assistant that understands MCP can integrate the server out of the box, reducing integration friction.

Key capabilities include:

• Comprehensive ticker information: Company profiles, financial statements, and real‑time trading metrics.
• News feeds: Recent articles tied to a symbol, with customizable counts.
• Search and discovery: Find stocks, ETFs, or other instruments via keyword queries, returning related news snippets.
• Top entities: Retrieve the highest‑performing companies, ETFs, or mutual funds within a specified sector.
• Historical price data: Flexible period and interval options (from one minute to yearly aggregates) for charting or trend analysis.
• Options chain: Calls, puts, or both for a given expiration date, enabling options strategy research.
• Earnings calendar: Upcoming earnings dates and historical results for informed forecasting.

In real‑world scenarios, financial analysts can use the server to power an AI‑driven research assistant that pulls live data into reports. Portfolio managers can query the server to evaluate risk exposure by fetching price histories and options liquidity. Educators may build interactive learning tools that let students explore market dynamics through AI prompts. Because the server is lightweight and purely data‑centric, it can be deployed behind corporate firewalls or in cloud functions, fitting neatly into existing AI workflows without adding complexity.