YFinance MCP Server

What does “YFinance” MCP Server do?

The YFinance MCP Server is a real-time stock information API built with Python, designed to serve as an MCP (Model Context Protocol) server. It allows AI assistants and clients to access live financial data and perform stock analysis by interfacing with the yfinance library. By acting as a bridge between AI and external sources of market data, the YFinance MCP Server enables workflows such as fetching real-time stock prices, accessing historical stock analysis, and integrating financial dashboards into applications. This enhances developer productivity by automating data retrieval and analysis for AI-powered tools, facilitating tasks like investment research, market tracking, and dynamic portfolio management.

List of Prompts

No prompt templates are explicitly mentioned in the repository.

List of Resources

No explicit MCP resource definitions are found in the available files.

List of Tools

No server.py file exists and tool definitions for MCP actions are not clearly listed in available files. However, the repository is described as providing stock price and stock analysis functionalities.

Use Cases of this MCP Server

• Real-Time Stock Price Retrieval: Developers can use the server to fetch up-to-date prices for a wide range of stocks, enabling financial dashboards and trading bots to make informed decisions.
• Historical Stock Analysis: The server interfaces with yfinance to provide historical price data, useful for backtesting trading strategies or generating market reports.
• Investment Research: AI agents can access the MCP server to retrieve detailed stock data during investment research tasks, improving the accuracy and depth of recommendations.
• Market Monitoring: Automated monitoring tools can use the server to track price changes and generate alerts for significant market events.
• Financial Dashboard Integration: The server can power widgets or dashboards with live and historical financial data, supporting visualization and analytics features.

How to set it up

No setup instructions or platform-specific guidance for Windsurf, Claude, Cursor, or Cline are provided in the repository files. There are no JSON configuration examples or environment variable instructions.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "yfinance-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “yfinance-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Roots support: Not specified

Based on the available information and the tables above, the YFinance MCP Server provides a clear overview and describes some practical use cases, but lacks technical documentation, explicit tool/resource definitions, and setup instructions. This limits its immediate usability for developers seeking a plug-and-play MCP server experience.

Our opinion

The MCP implementation is promising for finance-related AI workflows, but it lacks detailed documentation and explicit MCP constructs (prompts, tools, resources). The absence of setup instructions and configuration examples reduces its accessibility for most users. We’d rate this MCP server a 3/10 for completeness and developer friendliness.

MCP Score