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financial-datasets

Financial Datasets MCP Server

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README.md
# Financial Datasets MCP Server

## Introduction

This is a Model Context Protocol (MCP) server that provides access to stock market data from [Financial Datasets](https://www.financialdatasets.ai/). 

It allows Claude and other AI assistants to retrieve income statements, balance sheets, cash flow statements, stock prices, and market news directly through the MCP interface.

## Available Tools

This MCP server provides the following tools:
- **get_income_statements**: Get income statements for a company.
- **get_balance_sheets**: Get balance sheets for a company.
- **get_cash_flow_statements**: Get cash flow statements for a company.
- **get_current_stock_price**: Get the current / latest price of a company.
- **get_historical_stock_prices**: Gets historical stock prices for a company.
- **get_company_news**: Get news for a company.
- **get_available_crypto_tickers**: Gets all available crypto tickers.
- **get_crypto_prices**: Gets historical prices for a crypto currency.
- **get_historical_crypto_prices**: Gets historical prices for a crypto currency.
- **get_current_crypto_price**: Get the current / latest price of a crypto currency.

## Setup

### Prerequisites

- Python 3.10 or higher
- [uv](https://github.com/astral-sh/uv) package manager

### Installation

1. Clone this repository:
   ```bash
   git clone https://github.com/financial-datasets/mcp-server
   cd mcp-server
   ```

2. If you don't have uv installed, install it:
   ```bash
   # macOS/Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Windows
   curl -LsSf https://astral.sh/uv/install.ps1 | powershell
   ```

3. Install dependencies:
   ```bash
   # Create virtual env and activate it
   uv venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   
   # Install dependencies
   uv add "mcp[cli]" httpx  # On Windows: uv add mcp[cli] httpx

   ```

4. Set up environment variables:
   ```bash
   # Create .env file for your API keys
   cp .env.example .env

   # Set API key in .env
   FINANCIAL_DATASETS_API_KEY=your-financial-datasets-api-key
   ```

5. Run the server:
   ```bash
   uv run server.py
   ```

## Connecting to Claude Desktop

1. Install [Claude Desktop](https://claude.ai/desktop) if you haven't already

2. Create or edit the Claude Desktop configuration file:
   ```bash
   # macOS
   mkdir -p ~/Library/Application\ Support/Claude/
   nano ~/Library/Application\ Support/Claude/claude_desktop_config.json
   ```

3. Add the following configuration:
   ```json
   {
     "mcpServers": {
       "financial-datasets": {
         "command": "/path/to/uv",
         "args": [
           "--directory",
           "/absolute/path/to/financial-datasets-mcp",
           "run",
           "server.py"
         ]
       }
     }
   }
   ```
   
   Replace `/path/to/uv` with the result of `which uv` and `/absolute/path/to/financial-datasets-mcp` with the absolute path to this project.

4. Restart Claude Desktop

5. You should now see the financial tools available in Claude Desktop's tools menu (hammer icon)

6. Try asking Claude questions like:
   - "What are Apple's recent income statements?"
   - "Show me the current price of Tesla stock"
   - "Get historical prices for MSFT from 2024-01-01 to 2024-12-31"

TDQS

B3.3/5.0

Scored across 11 tools

Disambiguation4/5

Most tools have distinct purposes, but there is some potential confusion between get_crypto_prices and get_historical_crypto_prices, as their names and descriptions overlap significantly. Other tools are clearly differentiated by resource type (crypto vs. stock vs. financial statements).

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case naming. The pattern 'get_[resource]_[specific]' is applied uniformly across all 11 tools, making them predictable and easy to understand.

Tool Count5/5

With 11 tools, the server is well-scoped for financial data access. It covers crypto and stock prices, financial statements, news, and SEC filings, providing comprehensive coverage without being overwhelming or sparse.

Completeness4/5

The tool set covers key financial data domains well, including prices, statements, news, and filings. Minor gaps exist, such as no update or delete operations (which may not be needed) and limited filtering options for some tools, but core workflows are fully supported.

Maintenance

ActivityInactive
ResponsivenessUnresponsive