financial-datasets/mcp-server

Overview

The financial-datasets/mcp-server is an innovative Model Context Protocol (MCP) server designed to facilitate seamless interaction with the Financial Datasets stock market API. This server is particularly beneficial for developers and AI assistants like Claude, who need real-time access to critical stock market information such as income statements, balance sheets, cash flow statements, historical prices, and current news updates.

Key Features

• Get Income Statements: Effortlessly retrieve detailed income statements for any given stock symbol directly through simple command requests.
• Access Balance Sheets: Gain instant insights into company finances by fetching accurate balance sheet data showcasing assets versus liabilities.
• Retrieve Historical Prices: Analyze trends by accessing customizable historical price information based on specific date ranges and intervals.
• Stay Updated with Market News: Keep abreast of relevant developments affecting your investments by obtaining real-time news articles associated with particular stocks.

Setup Process

Setting up the MCP server is straightforward, making it accessible even to those who may not be experts in managing APIs or local servers. Here’s a step-by-step guide to get you started:

1. Clone the repository using git clone https://github.com/financial-datasets/mcp-server followed by cd mcp-server.
2. Install the uv package manager if you don’t have it already:
   • For macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh
   • For Windows: curl -LsSf https://astral.sh/uv/install.ps1 | powershell
3. Create a virtual environment and install project dependencies:

   uv venv  
   source .venv/bin/activate  # On Windows use .venv\Scripts\activate  
   uv add "mcp[cli]" httpx  # On Windows use uv add mcp[cli] httpx  
   

4. Set up secure environment variables by creating a .env file from the example template and inputting your unique FINANCIAL_DATASETS_API_KEY.
5. Run the local server using uv run server.py.
6. If using Claude Desktop, configure it to recognize this new MCP service by editing its config file.
7. Restart Claude Desktop once all configurations are complete.

Usability

The setup process is commendably straightforward, with clear instructions provided for cloning the repository, installing dependencies, and configuring environment variables. This empowers users to establish their own instance without getting bogged down by overly technical jargon.

Evaluation of Features

The core features of this MCP server are where its value truly lies. The ability to retrieve detailed income statements and balance sheets enhances transparency regarding a company's financial health, while customizable historical price access aids in analyzing trends over time. Furthermore, timely news updates about stocks keep investors informed about relevant developments affecting their portfolios.

Strengths and Weaknesses

While the functionalities offered by this MCP server are impressive, there are areas for improvement. One notable shortcoming is the lack of direct community engagement mentioned within the documentation. Clearer pathways toward community support, such as forums or discussion threads, would enhance collaboration opportunities. Additionally, while sample queries demonstrate capabilities effectively, more comprehensive documentation covering advanced query techniques could help power users maximize their interaction with these resources.

Conclusion

In summary, the financial-datasets/mcp-server offers powerful capabilities aimed at streamlining access to vital stock market data through advanced interfaces suitable for both developers and end-users leveraging intelligent assistants like Claude. This tool feels tailor-made for professionals operating within tech-heavy sectors focused on insights derived from reliable metrics sourced from reputable databases. While there’s room for improvement regarding community aspects, this MCP resource comes highly recommended for anyone engaged in stock market dynamics and investment decision-making.

License: The project is hosted on GitHub, and contributions are encouraged under the terms of the repository's license.

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