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MCP FinTechCo Server

A production-ready MCP server built with FastMCP 2.0 for financial technology applications

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "brett777-mcp-fintechco": {
      "url": "https://mcp.fintechco.example/mcp",
      "headers": {
        "FRED_API_KEY": "your-key-here",
        "ANTHROPIC_API_KEY": "your-key-here",
        "ALPHA_VANTAGE_API_KEY": "your-key-here"
      }
    }
  }
}

You are using the MCP FinTechCo Server to connect large language models with real-time financial data and economic indicators. Built on FastMCP 2.0, this server provides a production-ready interface to fetch stock quotes, FX rates, crypto prices, and a wide range of FRED economic data, with built-in technical indicators and testing utilities. This guide shows you how to use the server, install it locally, and configure essential options to start analyzing markets and economic trends with MCP-enabled clients.

How to use

You interact with the MCP FinTechCo Server through an MCP client. Start the server locally and connect your client to it to retrieve data, run indicators, and perform economic research. Use the following practical patterns to accomplish common goals:

- Retrieve real-time stock quotes with get_stock_quote for symbols like AAPL or MSFT. - Compute simple moving averages with get_sma and momentum with get_rsi for your chosen symbol and interval. - Get FX rates via get_fx_rate to compare currencies. - Access a wide range of economic indicators through FRED tools, including searches, releases, and time-series data with transformation options. - Combine market data with economic indicators to perform integrated analyses, such as tracking how unemployment releases relate to stock performance or inflation trends.

How to install

Prerequisites you need before installation are Python 3.11 or higher, pip, Git, and API keys for Alpha Vantage and FRED. You also need an environment to run Python applications locally.

python3 --version
pip --version
git --version

Install and run the server locally using these steps.

# 1. Clone the project repository
git clone https://github.com/YOUR-USERNAME/MCP-FinTechCo.git
cd MCP-FinTechCo

# 2. Create and activate a virtual environment
# Windows
python -m venv venv
venv\Scripts\activate
# Linux/Mac
python3 -m venv venv
source venv/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Configure environment variables
cp .env.sample .env
# Edit .env to add your API keys such as ALPHA_VANTAGE_API_KEY, FRED_API_KEY, ANTHROPIC_API_KEY

# 5. Run the server
python server.py

Configuration and usage notes

The server uses environment variables to control data access and runtime behavior. Key variables include the Alpha Vantage and FRED API keys used to fetch market data and economic indicators. You can adjust logging level and port for development or production deployments.

Example environment variables you will set in your .env file include:

ALPHA_VANTAGE_API_KEY=your-key-here
FRED_API_KEY=your-key-here
ANTHROPIC_API_KEY=your-key-here
MCP_SERVER_NAME=mcp-fintechco-server
MCP_SERVER_VERSION=1.0.0
MCP_SERVER_PORT=8000
LOG_LEVEL=INFO
ENVIRONMENT=development

Starting with custom configuration

You can start the server with a custom port or log level. For a development setup, set the port and log level before launching.

export MCP_SERVER_PORT=8080
export LOG_LEVEL=DEBUG
python server.py

Security and troubleshooting

Ensure API keys are set correctly in the environment and that you have network access to Alpha Vantage and FRED endpoints. If the server fails to start, verify Python version (3.11+), dependencies, and that the .env file is correctly configured.

Available tools

get_stock_quote

Get real-time stock quotes for a global equity symbol. Returns symbol, price, change, change_percent, volume, and trading details.

get_stock_daily

Retrieve daily OHLCV time series data for a stock symbol with options for compact or full history.

get_sma

Compute Simple Moving Average for a symbol over a specified interval and time period.

get_rsi

Calculate Relative Strength Index for momentum and overbought/oversold signals.

get_fx_rate

Fetch real-time foreign exchange rate between two currencies, including bid and ask prices.

get_crypto_rate

Get real-time cryptocurrency rates with market data and metadata.

get_city_weather

Demo tool returning current weather data for a given city to illustrate extensibility.

search_fred_series

Search the FRED database for economic indicators by keyword and return matching series.

search_series_tags

Discover categorization tags for economic series matching a query to aid discovery.

search_series_related_tags

Find related tags based on existing tag filters to refine queries.

get_fred_releases

List available FRED economic releases such as CPI, GDP, and employment data.

get_release_info

Get detailed information about a specific FRED release.

get_release_series

Get all economic series included in a specific FRED release.

get_release_dates

Fetch historical and upcoming release dates for a FRED release.

get_economic_indicator

Retrieve recent observations for a specific FRED series with optional date limits and count.

get_series_metadata

Obtain detailed metadata about a FRED series including units and frequency.

get_category_series

Return all economic series within a given FRED category.

get_series_observations

Fetch detailed observations with transformations for a FRED series.

get_series_vintagedates

Provide vintage dates showing when a FRED series was revised.