### Run finance-tools-mcp Server Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Starts the finance-tools-mcp server using the `uvx` command. This is the basic command to get the server running with its default configuration. ```bash uvx finance-tools-mcp ``` -------------------------------- ### Install finance-tools-mcp Project Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/architecture_plan.md These shell commands provide instructions for installing the `finance-tools-mcp` package. The first command is for general users, providing the core `mcp_server` functionality. The second command is for maintainers, installing the package along with optional development and data synchronization dependencies. ```Shell # User Installation pip install finance-tools-mcp # Maintainer Installation with optional dependencies uv pip install .[data-sync-worker,dev] ``` -------------------------------- ### Install uv Package Manager Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Installs the `uv` package manager, a fast Python package installer and resolver, using a curl script. This is a prerequisite for running the finance-tools-mcp server and managing its dependencies. ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` -------------------------------- ### Create a Flowchart with Mermaid Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/reports/vega.md This Mermaid snippet demonstrates how to create a simple flowchart with decision points. It illustrates basic node and edge definitions, including conditional paths for 'Yes' and 'No' outcomes. ```Mermaid graph TD A[Start] --> B{Decision} B -->|Yes| C[Action 1] B -->|No| D[Action 2] C --> E[End] D --> E ``` -------------------------------- ### Configure MCP Client for finance-tools-mcp Development Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md JSON configuration for Claude Desktop to test the finance-tools-mcp server during local development. This setup points the client to the local `uv` command and the project directory for direct execution. ```json { "mcpServers": { "investor": { "command": "path/to/uv/command/uv", "args": ["--directory", "path/to/finance-tools-mcp", "run", "finance-tools-mcp"] } } } ``` -------------------------------- ### Define CLI Entry Points in pyproject.toml Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/architecture_plan.md This `pyproject.toml` section specifies command-line interface (CLI) entry points for the `mcp_server` and `data_sync_worker` applications. These entries allow users and maintainers to run the applications directly from the command line after installation. It also notes the planned replacement or adaptation of an older entry point. ```TOML [project.scripts] mcp_server_cli = "apps.mcp_server.cli:main" data_sync_worker_cli = "apps.data_sync_worker.cli:main" # The existing 'finance-tools-mcp = "src.investor_agent.server:main"' will be replaced or adapted. ``` -------------------------------- ### Run finance-tools-mcp with SSE Transport Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Starts the finance-tools-mcp server configured to use Server-Sent Events (SSE) for transport. This option changes the communication protocol used by the server, potentially for real-time updates. ```bash uvx finance-tools-mcp --transport sse ``` -------------------------------- ### Run finance-tools-mcp with FRED API Key and SSE Transport Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Combines setting the FRED API key with using SSE transport when starting the finance-tools-mcp server. This provides both custom data access and a specific communication protocol for advanced usage. ```bash FRED_API_KEY=YOUR_API_KEY uvx finance-tools-mcp --transport sse ``` -------------------------------- ### Generate a Bar Chart with Vega-Lite Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/reports/vega.md This Vega-Lite JSON specification defines a simple bar chart. It includes embedded data, sets the mark type to 'bar', and specifies encoding for x (category) and y (quantitative value) axes, along with a fixed chart width. ```Vega-Lite { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "description": "A simple bar chart with embedded data.", "data": { "values": [ {"category": "A", "value": 28}, {"category": "B", "value": 55}, {"category": "C", "value": 43}, {"category": "D", "value": 91} ] }, "mark": "bar", "encoding": { "x": {"field": "category", "type": "nominal", "axis": {"labelAngle": 0}}, "y": {"field": "value", "type": "quantitative"} }, "width": 400 } ``` -------------------------------- ### Get Earnings History (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Provides a detailed history of a company's earnings, including actual reported earnings, analyst estimates, and the surprise percentage for each quarter. This helps in analyzing past performance against expectations. ```APIDOC get_earnings_history(ticker: str) ticker: The stock ticker symbol. Returns: Earnings history with estimates and surprises. ``` -------------------------------- ### Get Current Time (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Provides the current date and time. This simple utility can be used for timestamping or time-sensitive operations. ```APIDOC get_current_time() Returns: The current date and time. ``` -------------------------------- ### Get Current Fear & Greed Index (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Retrieves the current score and rating of the CNN Fear & Greed Index. This index measures two primary emotions that drive investors to pay too much or too little for stocks. ```APIDOC get_current_fng_tool() Returns: The current CNN Fear & Greed Index score and rating. ``` -------------------------------- ### Run finance-tools-mcp with FRED API Key Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Launches the finance-tools-mcp server while setting the `FRED_API_KEY` environment variable. This allows the server to access FRED data using a custom API key for financial data retrieval. ```bash FRED_API_KEY=YOUR_API_KEY uvx finance-tools-mcp ``` -------------------------------- ### Configure MCP Client for finance-tools-mcp Usage Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Provides a JSON configuration snippet for integrating the finance-tools-mcp server with an MCP client, such as Claude Desktop. It specifies the command and arguments required to launch the server from the client's environment. ```json { "mcpServers": { "investor": { "command": "path/to/uvx/command/uvx", "args": ["finance-tools-mcp"] } } } ``` -------------------------------- ### Configure Hatchling Packaging in pyproject.toml Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/architecture_plan.md This configuration snippet for `pyproject.toml` defines how the `finance-tools-mcp` project's components (`investor_agent_lib`, `mcp_server`, `data_sync_worker`) are included in the PyPI wheel package. It uses Hatchling's `packages` directive to specify the directories to be bundled. ```TOML [tool.hatch.build.targets.wheel] packages = ["packages/investor_agent_lib", "apps/mcp_server", "apps/data_sync_worker"] ``` -------------------------------- ### Debug finance-tools-mcp with MCP Inspector Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Uses the `@modelcontextprotocol/inspector` tool to debug the finance-tools-mcp server. Two common methods are provided: one directly launching via `uvx` and another specifying the `uv` directory for local development. ```bash npx @modelcontextprotocol/inspector uvx finance-tools-mcp ``` ```bash npx @modelcontextprotocol/inspector uv --directory ./ run finance-tools-mcp ``` -------------------------------- ### Visualize User Workflow for finance-tools-mcp Monorepo Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/architecture_plan.md This Mermaid diagram illustrates the end-to-end user workflow within the `finance-tools-mcp` monorepo. It depicts how the maintainer generates and hosts the `market_data.sqlite` database using the `data_sync_worker` and `investor_agent_lib`, and how end-users download this database to run the `mcp_server` application, which then serves data to their API clients. ```Mermaid graph TD subgraph MaintainerInfrastructure [Maintainer's Environment] direction LR ext_api["External Market Data API"] ds_worker_app["apps/data_sync_worker"] core_lib_maintainer["packages/investor_agent_lib"] generated_db["market_data.sqlite (Generated Daily)"] db_file_hosting["DB File Hosting (e.g., S3, Web Server)"] ext_api --> ds_worker_app core_lib_maintainer --> ds_worker_app ds_worker_app --> generated_db generated_db --> db_file_hosting end subgraph UserComputer [User's Computer] direction LR user_downloaded_db["market_data.sqlite (Downloaded by User)"] mcp_server_app["apps/mcp_server (Run by User)"] core_lib_user["packages/investor_agent_lib (Used by mcp_server)"] user_api_clients["User's API Clients / MCP Consumers"] core_lib_user --> mcp_server_app user_downloaded_db --> mcp_server_app mcp_server_app --> user_api_clients end db_file_hosting -- User Downloads DB File --> user_downloaded_db ``` -------------------------------- ### Define Monorepo Directory Structure for finance-tools-mcp Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/architecture_plan.md This snippet outlines the proposed monorepo directory structure for `finance-tools-mcp`. It organizes the project into `packages` for reusable libraries (e.g., `investor_agent_lib`) and `apps` for runnable applications (e.g., `mcp_server`, `data_sync_worker`), along with root configuration files and data directories. This structure aims to improve code sharing and simplify dependency management. ```Filesystem finance-tools-mcp/ ├── pyproject.toml ├── uv.lock ├── README.md ├── LICENSE ├── .gitignore ├── packages/ │ └── investor_agent_lib/ │ ├── __init__.py │ ├── yfinance_tools.py │ ├── calc_basic_statistics.py │ └── ... ├── apps/ │ ├── mcp_server/ │ │ ├── __init__.py │ │ ├── main.py │ │ ├── cli.py │ │ └── ... │ └── data_sync_worker/ │ ├── __init__.py │ ├── main.py │ ├── cli.py │ └── ... ├── data/ │ └── market_data.sqlite └── reports/ ``` -------------------------------- ### Mermaid Pie Chart for Recommended Portfolio Allocation Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/reports/meli_se_shop_comparison_20250504.md This Mermaid diagram illustrates the recommended portfolio allocation percentages for MELI, SE, and SHOP based on the comprehensive analysis. It visually represents the suggested weighting for each stock within an investment portfolio. ```Mermaid pie title Recommended Portfolio Allocation "MELI": 55 "SE": 25 "SHOP": 20 ``` -------------------------------- ### Vega-Lite Chart for Fundamental Metrics Comparison Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/reports/meli_se_shop_comparison_20250504.md This Vega-Lite specification generates a bar chart comparing key fundamental metrics (Revenue Growth, Profit Margin, P/E Ratio) for MELI, SE, and SHOP. It visualizes company performance across these financial indicators to aid in fundamental analysis. ```Vega-Lite { "$schema": "https://vega.github.io/vega-lite/v5.json", "description": "Fundamental Metrics Comparison for MELI, SE, and SHOP", "data": { "values": [ {"Metric": "Revenue Growth", "Company": "MELI", "Value": 51.5}, {"Metric": "Revenue Growth", "Company": "SE", "Value": 5.9}, {"Metric": "Revenue Growth", "Company": "SHOP", "Value": 19.6}, {"Metric": "Profit Margin", "Company": "MELI", "Value": 9.2}, {"Metric": "Profit Margin", "Company": "SE", "Value": 2.6}, {"Metric": "Profit Margin", "Company": "SHOP", "Value": 22.7}, {"Metric": "P/E Ratio", "Company": "MELI", "Value": 60.6}, {"Metric": "P/E Ratio", "Company": "SE", "Value": 191.6}, {"Metric": "P/E Ratio", "Company": "SHOP", "Value": 64.0} ] }, "mark": "bar", "encoding": { "x": {"field": "Company", "type": "nominal", "axis": null}, "y": {"field": "Value", "type": "quantitative"}, "color": {"field": "Company", "type": "nominal"}, "column": {"field": "Metric", "type": "nominal", "header": {"titleOrient": "bottom", "labelOrient": "bottom"}} }, "config": { "categoryField": {"title": "Metric"} }, "width": 100 } ``` -------------------------------- ### Search FRED Series by Keyword (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Searches for popular FRED series based on a provided keyword. This helps in discovering relevant macroeconomic data series when the exact ID is unknown. ```APIDOC search_fred_series(keyword: str) keyword: The keyword to search for (e.g., 'inflation', 'unemployment'). Returns: A list of popular FRED series matching the keyword. ``` -------------------------------- ### Mermaid Pie Chart for Recent Institutional Actions Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/reports/meli_se_shop_comparison_20250504.md This Mermaid diagram visualizes the recent institutional buying and selling activity for MELI, SE, and SHOP. It provides a quick overview of net institutional flow for each company, highlighting accumulation or outflow trends. ```Mermaid pie title Recent Institutional Actions "MELI Buyers": 45 "MELI Sellers": 35 "SE Buyers": 38 "SE Sellers": 42 "SHOP Buyers": 40 "SHOP Sellers": 50 ``` -------------------------------- ### Retrieve Historical Fear & Greed Index (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Fetches historical data for the CNN Fear & Greed Index over a specified number of days. This allows for analysis of past sentiment trends. ```APIDOC get_historical_fng_tool(days: int) days: The number of past days for which to retrieve data. Returns: Historical CNN Fear & Greed Index data. ``` -------------------------------- ### Fetch Latest Breaking World News (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Retrieves the latest breaking world news headlines from major news sources like CNBC, BBC, and SCMP. This provides a broad overview of global events. ```APIDOC cnbc_news_feed() Returns: The latest breaking world news headlines. ``` -------------------------------- ### Retrieve FRED Series Data (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Fetches data for a specific series from the Federal Reserve Economic Data (FRED) database. This allows access to a wide range of macroeconomic data. ```APIDOC get_fred_series(series_id: str) series_id: The FRED series ID (e.g., 'GDP', 'CPIAUCSL'). Returns: Data for the specified FRED series. ``` -------------------------------- ### Fetch Historical Price Data Digest (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Retrieves historical price data for a specified period, optimized for LLM consumption. The output includes OHLCV samples, various technical indicators, risk metrics, and other quantitative analyses, compiled into a structured digest format. ```APIDOC get_price_history(ticker: str, period: str) ticker: The stock ticker symbol. period: The historical period (e.g., '1y', '5y', 'max'). Returns: A structured digest of historical price data, technical indicators, and risk metrics. ``` -------------------------------- ### Access Financial Statements (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Provides access to a company's financial statements, including income statements, balance sheets, and cash flow statements. Data can be retrieved on a quarterly or annual basis for a given ticker. ```APIDOC get_financial_statements(ticker: str, statement_type: str, frequency: str = 'quarterly') ticker: The stock ticker symbol. statement_type: Type of statement ('income', 'balance', 'cash_flow'). frequency: Data frequency ('quarterly' or 'annually'). Returns: The requested financial statement data. ``` -------------------------------- ### Evaluate Mathematical Expressions (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Evaluates mathematical expressions using Python's built-in math syntax and NumPy capabilities. This tool can perform complex calculations based on provided expressions. ```APIDOC calculate(expression: str) expression: The mathematical expression to evaluate (e.g., 'sqrt(25) + log(10)'). Returns: The result of the evaluated expression. ``` -------------------------------- ### Retrieve Comprehensive Ticker Data Report (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Provides a comprehensive financial report for a given stock ticker. This tool aggregates various data points including company overview, latest news, key financial metrics, performance indicators, important dates, analyst recommendations, and recent upgrades/downgrades, offering a holistic view of the ticker's status. ```APIDOC get_ticker_data(ticker: str) ticker: The stock ticker symbol (e.g., 'AAPL'). Returns: A comprehensive report object containing various financial insights. ``` -------------------------------- ### Retrieve Options Data for a Ticker (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Fetches options data with the highest open interest for a specified stock ticker. The retrieval can be filtered by date range, strike price, and the type of option (Calls or Puts) to narrow down the results. ```APIDOC get_options(ticker: str, date_range: Optional[str] = None, strike_price: Optional[float] = None, option_type: Optional[str] = None) ticker: The stock ticker symbol. date_range: Optional date range for options data (e.g., '1y', '3m'). strike_price: Optional specific strike price to filter by. option_type: Optional filter for 'Calls' or 'Puts'. Returns: Options data with the highest open interest. ``` -------------------------------- ### Fetch Latest Ticker News (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Retrieves the latest news articles specifically related to a given stock ticker from Yahoo Finance. This tool helps in staying updated with company-specific developments. ```APIDOC get_ticker_news_tool(ticker: str) ticker: The stock ticker symbol. Returns: The latest Yahoo Finance news articles for the ticker. ``` -------------------------------- ### Analyze Fear & Greed Index Trend (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Analyzes the trends in the CNN Fear & Greed Index over a specified period. This tool helps in understanding the evolving market sentiment. ```APIDOC analyze_fng_trend(period: str) period: The period over which to analyze the trend (e.g., '30d', '90d'). Returns: An analysis of the Fear & Greed Index trend. ``` -------------------------------- ### Retrieve Insider Trading Activity (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Fetches recent insider trading activity for a given stock ticker. This includes details on purchases and sales made by company executives and major shareholders. ```APIDOC get_insider_trades(ticker: str) ticker: The stock ticker symbol. Returns: Recent insider trading activity data. ``` -------------------------------- ### List Institutional Holders (APIDOC) Source: https://github.com/voxlink-org/finance-tools-mcp/blob/main/README.md Retrieves a list of major institutional and mutual fund holders for a specified stock ticker. This provides insight into significant ownership stakes in the company. ```APIDOC get_institutional_holders(ticker: str) ticker: The stock ticker symbol. Returns: A list of institutional and mutual fund holders. ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.