The Finance Tools MCP server provides comprehensive financial data analysis and research tools:
Ticker Data Tools: Access detailed ticker reports, options data (filterable by date/strike/type), historical price data with technical indicators and risk metrics, financial statements (income, balance, cash flow), institutional holders, earnings history (with estimates and surprises), insider trades, and news.
Fear & Greed Index Tools: Retrieve current and historical CNN Fear & Greed Index data, and analyze market sentiment trends.
Calculation Tools: Evaluate mathematical expressions using Python and NumPy, and calculate technical indicators with TA-lib (optionally using cached ticker data).
Macro Data Tools: Access current time, FRED database information (GDP, CPI, etc.), and latest breaking news from CNBC, BBC, and SCMP.
Fetches and analyzes Fear & Greed Index data from CNN, providing current values, historical trends, and market sentiment analysis for investment decision-making.
Leverages data from GitHub repositories to provide financial analysis tools, with the finance-tools-mcp being derived from the investor-agent GitHub project.
Integrates with PyPI packages including yfinance for market data retrieval, ta-lib-python for technical indicators, and fredapi for macroeconomic indicators.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Finance Tools MCP show me the latest stock price and analyst ratings for Apple"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The finance-tools-mcp is a Model Context Protocol (MCP) server designed to provide comprehensive financial insights and analysis capabilities to Large Language Models (LLMs). Modified from investor-agent, it integrates with various data sources and analytical libraries to offer a suite of tools for detailed financial research and analysis.
Related MCP server: Trading MCP Server
Python: 3.10 or higher is required.
Package Manager: uv is the recommended package installer and resolver for this project.
First, install uv if you haven't already:
curl -LsSf https://astral.sh/uv/install.sh | shEnsure uv is in your system's PATH. You might need to restart your terminal or add ~/.cargo/bin to your PATH.
Then, you can run the finance-tools-mcp MCP server using uvx (which executes a package without explicitly installing it into your environment):
uvx finance-tools-mcpTo run the server with a FRED API key (for enhanced macroeconomic data access), set it as an environment variable:
FRED_API_KEY=YOUR_API_KEY uvx finance-tools-mcpYou can also run the server using Server-Sent Events (SSE) transport, which might be preferred by some MCP clients:
uvx finance-tools-mcp --transport sseOr with both the FRED API key and SSE transport:
FRED_API_KEY=YOUR_API_KEY uvx finance-tools-mcp --transport sseTo integrate finance-tools-mcp with an MCP client (for example, Claude Desktop), add the following configuration to your claude_desktop_config.json:
{
"mcpServers": {
"investor": {
"command": "path/to/uvx/command/uvx",
"args": ["finance-tools-mcp"],
}
}
}You can leverage the MCP inspector to debug the server:
npx @modelcontextprotocol/inspector uvx finance-tools-mcpor
npx @modelcontextprotocol/inspector uv --directory ./ run finance-tools-mcpFor log monitoring, check the following directories:
macOS: ~/Library/Logs/Claude/mcp*.log
Windows: %APPDATA%\Claude\logs\mcp*.log
For local development and testing:
Use the MCP inspector as described in the Debugging section.
Test using Claude Desktop with this configuration:
{
"mcpServers": {
"investor": {
"command": "path/to/uv/command/uv",
"args": ["--directory", "path/to/finance-tools-mcp", "run", "finance-tools-mcp"],
}
}
}Analyze trends in CNN Fear & Greed Index over specified days.
Parameters:
days (int): Number of days to analyze (limited by available data).
Returns:
str: A string containing the analysis results, including latest value,
average value, trend direction, and number of data points analyzed.
Inputs: days (required).
Calculate the result of a mathematical expression. Support python math syntax and numpy. > calculate("2 * 3 + 4") {'result': 10} > calculate("sin(pi/2)") {'result': 1.0} > calculate("sqrt(16)") {'result': 4.0} > calculate("np.mean([1, 2, 3])") {'result': 2.0}
Inputs: expression (required).
Get the latest breaking world news from CNBC, BBC, and SCMP. Useful to have an overview for the day. Include the Fed rate prediction from Fed watch and key macro indicators.
Inputs: no named parameters.
Get the current time in ISO 8601 format.
Inputs: no named parameters.
Get earnings history with estimates and surprises.
Inputs: ticker (required).
Get financial statements. Types: income, balance, cash. Frequency: quarterly, annual.
Inputs: ticker (required), frequency, statement_type.
Get a FRED series by its ID. However the data is not always the latest, so use with caution!!!
Inputs: series_id (required).
Get historical CNN Fear & Greed Index data for a specified number of days.
Parameters:
days (int): Number of days of historical data to retrieve (limited by the API).
Returns:
str: Historical Fear & Greed Index values for the specified period.
Inputs: days (required).
Get recent insider trading activity.
Inputs: ticker (required).
Get comprehensive market sentiment indicators including:
- CNN Fear & Greed Index (score and rating)
- Market RSI (Relative Strength Index)
- VIX (Volatility Index)
Returns:
str: Formatted string containing all three indicators with their current values
Inputs: no named parameters.
Get historical price data digest for specified period. There're two kinds of response mode: 1. The period mode. It will generate a digest for LLM consumption. Usually get at least 3 months, 6 months or more. The response includes OCHLCV samples, Technical Indicators (by ta-lib) , Risk Metrics, and other quantitative analysis. 2. The start_date and end_date mode. Once the start_date (yyyy-mm-dd) and end_date (yyyy-mm-dd) are specified, it will generate a raw OCHLCV data in the slot. And no digest will be generated in this mode. Useful for checking the price history of a specific short date range.
Inputs: ticker (required), end_date, period, start_date.
Get comprehensive report for ticker: overview, news, metrics, sector / industry valuation, performance, dates, analyst recommendations, and upgrades/downgrades.
Inputs: ticker (required).
For getting yahoo financial news of a ticker. Useful for getting latest news, especially for doing deep research.
Inputs: ticker (required).
Get top 25 institutional holders and their changes for a given stock ticker.
Inputs: ticker (required).
Search for the most popular FRED series by keyword. Useful for finding key data by name. Like GDP, CPI, etc. However the data is not always the latest.
Inputs: query (required).
Analyzes and summarizes option data for a given ticker.
This function retrieves option indicators and Greeks for the specified ticker,
generates a digest summarizing key metrics, and formats a table of key option
data including last trade date, strike, option type, open interest, volume,
and implied volatility.
Args:
ticker: Stock ticker symbol.Inputs: ticker (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 17 tools
Most tools have distinct purposes, but there is some overlap that could cause confusion. For example, get_historical_fng_tool and analyze_fng_trend both deal with Fear & Greed Index data, and get_ticker_data includes news while get_ticker_news_tool is specifically for news. However, descriptions help clarify differences, such as analyze_fng_trend focusing on trend analysis versus get_historical_fng_tool for raw data retrieval.
The naming follows a consistent snake_case pattern with a verb_noun structure for most tools, like get_price_history and search_fred_series. There are minor deviations, such as calculate being a single verb and cnbc_news_feed using a noun_verb_noun pattern, but overall, the naming is predictable and readable.
With 17 tools, the count is slightly high but reasonable for a finance domain that covers diverse areas like market data, news, sentiment, and analysis. It provides comprehensive coverage without being overwhelmingly large, though it borders on the heavy side for typical MCP server scopes.
The tool set offers broad coverage for financial analysis, including data retrieval (e.g., price history, financial statements), sentiment indicators, news feeds, and specialized tools like options analysis. Minor gaps exist, such as no explicit tools for portfolio management or trading actions, but agents can work around this with the available tools for core financial workflows.
Official Intrinio MCP server. US stock prices, options and fundamentals for AI agents.
MCP server for Financial Modeling Prep API with 250+ financial data tools
The Octagon MCP server provides specialized AI-powered financial research and analysis by integrating with the Octagon Market Intelligence API. It enables users to analyze public market data (SEC filings, earnings transcripts, financial metrics, and stock data for 8000+ companies), private market data (3M+ companies, 500k+ funding rounds, 2M+ M&A/IPO transactions), and conduct deep research including web scraping capabilities. The server also features autonomous research agents that search hundreds of sources and return fully cited reports in approximately one minute.
7-factor stock scoring MCP server. US/HK/CN, 74 stocks. Free + Premium (USDC/Base). x402 ready.
social_media_feedA
Get most discussed stocks and investments opinions from reddit. Useful to know what investors are talking about. keywords is optional. Set keywords to match the specific topic you are interested in, by 'OR' operator, e.g. ['tsla', 'tesla'], ['tesla', 'spacex'], ['AAPL', 'apple', 'tim cook', 'cook'] No keywords will return the most discussed stocks and investments.
Inputs: keywords.