yahoo-finance

This MCP server exposes Yahoo Finance data to your AI agents through a set of ready-made tools. You can fetch detailed ticker information, recent news, and price history; search for equities, ETFs, and funds; retrieve top entities by sector; and access option chains and earnings data. The server runs asynchronously to keep AI responses fast, and it supports proxies if you need to route traffic through a proxy network. Typical usage involves sending structured tool calls like { name: "get-ticker-info", arguments: { symbol: "AAPL" } } or { name: "search", arguments: { query: "Tesla", count: 10 } } and receiving JSON responses with the requested data. Tools include: get-ticker-info for company details and metrics; get-ticker-news for related articles; search for instruments with related news; get-top-entities by sector; get-price-history for historical data with selectable periods and intervals; ticker-option-chain for options data; and ticker-earning for earnings information. You can also configure a PROXY_URL to improve reliability or bypass rate limits. Use the MCP inspector to test interactively or invoke tools directly from your AI agent as part of your decision loop.

Prerequisites:

• Python 3.11 or higher -pip (or uvx) for package management
• (Optional) Proxy server for reliability

Installation steps:

1. Install the package from PyPI:

   pip install yahoo-finance-server
   

2. Run the server locally (uses the module installed by the package):

   yahoo-finance-server
   

3. (Optional) If you prefer to run via uvx, you can install the package using uvx and run a similar command depending on the entry point provided by the package:

   uvx yahoo-finance-server
   

4. If you want to run from source (development)

   git clone https://github.com/AgentX-ai/AgentX-mcp-servers.git
   cd AgentX-mcp-servers/yahoo_finance_server
   pip install -e .
   

5. Optional: configure a proxy to improve reliability

   export PROXY_URL="http://proxy.example.com:8080"
   yahoo-finance-server
   

Tips and common considerations:

• Proxy support: Set PROXY_URL to route requests through HTTP/HTTPS or SOCKS proxies for reliability and rate-limit avoidance.
• Environment variables: The server reads PROXY_URL if you need proxying; other variables may be introduced by future updates—check the docs if behavior changes.
• Data freshness: Yahoo Finance data is updated with market hours; plan caching strategies if your AI requires very fresh quotes.
• API surface: The provided tools map to common finance data needs; if you need additional endpoints (e.g., more granular historical intervals), consider extending the MCP server or contributing a feature.
• Debugging: Use the MCP Inspector with npx @modelcontextprotocol/inspector yahoo-finance-server to validate tool calls and responses during development.