> ## Documentation Index > Fetch the complete documentation index at: https://docs.dappier.com/llms.txt > Use this file to discover all available pages before exploring further. # 🧩 Stock Research & Investment Strategy Agent with LangChain + Dappier MCP using `mcp-use` This cookbook demonstrates how to build a real-time stock analysis and investment planning assistant using **LangChain**, **Dappier MCP**, and the lightweight `**mcp-use**` client. By integrating live financial data through the **Model Context Protocol (MCP)**, this guide walks through constructing agents that deliver in-depth market insights and actionable strategies. In this cookbook, you'll explore: * **LangChain + OpenAI**: A modular framework to build LLM-powered applications with support for agents, tools, and memory. * **Dappier MCP**: A Model Context Protocol server that connects your agents to real-time, rights-cleared, AI-powered tools such as financial news, live stock data, and market summaries. * **mcp-use**: A lightweight Python client that bridges any LLM with any MCP server using standard transports like `stdio` and `http`—without relying on proprietary SDKs. * **Stock Analysis & Investment Strategy**: A real-world use case where the assistant performs deep research on a specific stock ticker and generates a strategic investment plan based on current market data. This example demonstrates a flexible architecture for building real-time, tool-augmented assistants and lays the foundation for other real-world applications powered by dynamic context, tool use, and AI reasoning. ## 📦 Installation To get started, install the required tools and dependencies: *** **Step 1: Install `uv` (required to run the Dappier MCP server)** **macOS / Linux**: ```bash theme={null} curl -LsSf https://astral.sh/uv/install.sh | sh ``` **Windows**: ```bash theme={null} powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` *** **Step 2: Create a Virtual Environment (Recommended)** Create and activate a virtual environment to isolate dependencies. **macOS / Linux**: ```bash theme={null} python3 -m venv venv source venv/bin/activate ``` **Windows**: ```bash theme={null} python -m venv venv venv\Scripts\activate ``` *** **Step 3: Install Python Packages** Install the necessary Python dependencies including LangChain and `mcp-use`. ```bash theme={null} pip install langchain-openai mcp-use python-dotenv ``` ## 🔑 Setting Up API Keys You’ll need API keys for both **Dappier** and **OpenAI** to authenticate your requests and access tools. *** **Dappier API Key** Visit [Dappier](https://platform.dappier.com/profile/api-keys) to generate your API key. Dappier provides free credits to help you get started. You can set the API key as an environment variable in your terminal: ```bash theme={null} export DAPPIER_API_KEY=your-dappier-api-key ``` Or include it in a `.env` file at the root of your project: ```env theme={null} DAPPIER_API_KEY=your-dappier-api-key ``` *** **OpenAI API Key** Go to [OpenAI](https://platform.openai.com/account/api-keys) to retrieve your API key. Set it in your terminal: ```bash theme={null} export OPENAI_API_KEY=your-openai-api-key ``` Or include it in your `.env` file: ```env theme={null} OPENAI_API_KEY=your-openai-api-key ``` In your Python script, load the `.env` file: ```python Python theme={null} from dotenv import load_dotenv load_dotenv() ``` ## ⚙️ Import Dependencies Start by importing the required modules to build the stock research agent. This includes components from `mcp-use`, LangChain, and environment configuration. ```python Python theme={null} import asyncio import os from dotenv import load_dotenv from langchain_openai import ChatOpenAI from mcp_use import MCPAgent, MCPClient ``` These imports enable: * Loading API keys from the environment using `dotenv` * Running asynchronous workflows with `asyncio` * Accessing OpenAI models via LangChain * Connecting to the Dappier MCP server and executing tool-augmented financial research using `mcp-use` ## 📝 Define User Input We’ll collect the name of the company or stock from the user, which will be used to perform real-time research and analysis. ```python Python theme={null} def get_user_input(): stock_name = input("Enter the name of the company or stock: ").strip() return stock_name ``` ## 🛰️ Run the Agent with Dappier MCP This function sets up the MCP agent using `mcp-use`, formulates the stock research query, and executes it using real-time financial tools provided by the Dappier MCP server. ```python Python theme={null} async def run_stock_research(stock_name: str): # Load environment variables load_dotenv() # Retrieve API key api_key = os.getenv("DAPPIER_API_KEY") if not api_key: raise ValueError("DAPPIER_API_KEY is not set") # MCPClient configuration using stdio transport (via uvx dappier-mcp) config = { "mcpServers": { "dappier": { "command": "uvx", "args": ["dappier-mcp"], "env": { "DAPPIER_API_KEY": api_key } } } } # Create MCP client and LLM client = MCPClient.from_dict(config) llm = ChatOpenAI(model="gpt-4o") # Create the agent agent = MCPAgent(llm=llm, client=client, max_steps=30) try: # Formulate query query = f""" Conduct a comprehensive investment research on the stock: {stock_name}. Follow these steps: 1. Use Dappier's real-time search to gather the latest financial news related to {stock_name}. 2. Retrieve recent trading data and market performance for the stock. 3. Analyze sentiment and performance trends from recent investor commentary or news coverage. 4. Generate a detailed stock analysis, including historical trends, recent developments, and key performance indicators. 5. Based on the research, determine whether this is a good time to invest or if it's better to wait. If investing now, provide guidance on: - A suitable target sell price or time frame for selling - An appropriate stop-loss level to manage downside risk 6. If the recommendation is to wait, explain the indicators or conditions to monitor before investing. Ensure the final output includes: - A structured summary of findings - Relevant real-time news links - Clear investment strategy recommendation with timing and risk management considerations """ print("\n" + "-" * 40) print(f"Running stock analysis for: {stock_name}") print("\n=== Response ===\n") result = await agent.run(query, max_steps=30) print(result) finally: # Clean up sessions if client.sessions: await client.close_all_sessions() ``` ## 🚦 Initialize and Launch the Workflow The `main()` function collects user input, then launches the asynchronous workflow to run the stock research agent using `mcp-use` and Dappier MCP. ```python Python theme={null} async def main(): stock_name = get_user_input() await run_stock_research(stock_name) ``` To start the agent, run the main function using `asyncio`: ```python Python theme={null} if __name__ == "__main__": asyncio.run(main()) ``` ```json theme={null} Enter the name of the company or stock: TSLA ---------------------------------------- Running stock analysis for: TSLA === Response === [04/23/25 13:35:25] INFO Processing request of type ListToolsRequest server.py:534 INFO Processing request of type ListToolsRequest server.py:534 [04/23/25 13:35:27] INFO Processing request of type CallToolRequest server.py:534 [04/23/25 13:35:31] INFO HTTP Request: POST https://api.dappier.com/app/aimodel/am_01j749h8pbf7ns8r1bq9s2evrh "HTTP/1.1 200 OK" _client.py:1025 INFO Processing request of type CallToolRequest server.py:534 [04/23/25 13:35:37] INFO HTTP Request: POST https://api.dappier.com/app/aimodel/am_01j749h8pbf7ns8r1bq9s2evrh "HTTP/1.1 200 OK" _client.py:1025 [04/23/25 13:35:38] INFO Processing request of type CallToolRequest server.py:534 [04/23/25 13:36:04] INFO HTTP Request: POST https://api.dappier.com/app/aimodel/am_01j749h8pbf7ns8r1bq9s2evrh "HTTP/1.1 200 OK" _client.py:1025 Here's a comprehensive analysis and investment research on Tesla, Inc. (TSLA): ### Summary of Findings 1. **Latest Financial News:** - **Challenges and Strategic Decisions:** Tesla is facing challenges termed as a "mini disaster" with concerns from investors about the company's handling of macroeconomic factors and earnings ([Motley Fool](https://www.fool.com/investing/2023/10/22/where-does-tesla-stock-go-from-here-after-the-mini/)). - **Earnings and Investor Sentiment:** Elon Musk's management and communication strategies have sparked mixed reactions among investors ([Benzinga](https://www.benzinga.com/analyst-ratings/analyst-color/23/10/35365351/tesla-investor-says-elon-musk-overplayed-macro-card-as-he-details-game-plan-to-lift)). - **Energy Business Growth:** Tesla’s energy business has shown significant growth, outpacing its automotive segment, which provides diversification and additional revenue streams ([Motley Fool](https://www.fool.com/investing/2023/10/22/tsla-energy-business-q3-earnings/)). 2. **Recent Trading Data and Market Performance:** - **Current Price:** $259.60 - **Recent Trade Prices:** Range between $259.52 and $259.58, indicating very minimal fluctuations at the time of the latest trade report. 3. **Sentiment and Performance Trends:** - Recent investor commentary highlights a cautious sentiment due to perceived overplaying of external economic conditions by management. - Strong growth in the energy sector is seen as a positive diversifier for the company. 4. **Stock Analysis:** - **Historical Trends:** Tesla has been a high-growth stock with significant fluctuations influenced by market sentiment and broader economic conditions. - **Recent Developments:** Positive growth in non-automotive sectors like energy, with some concerns over automotive sales and market share. - **Key Performance Indicators:** Best observed in the expanding energy division, boosting overall profitability. ### Investment Strategy Recommendation Based on the analysis, if investing now, consider the following guidance: - **Target Sell Price/Time Frame:** Given the current volatility and positive long-term outlook in energy, a target stall price of around $300 over the next 6-12 months could be reasonable, assuming market conditions improve. - **Stop-Loss Level:** A prudent stop-loss level would be around $240, given recent price support levels. If opting to **wait before investing**, monitor these key indicators or conditions: - **Market Conditions and Macro Environment:** Watch for stability in economic indicators that could mitigate current investor worries. - **Automotive Sales Performance:** Look for signs of growth recovery or market share retention in Tesla's primary automotive business. - **Performance of the Energy Sector:** Continued robust growth in Tesla's energy division can bolster investor confidence significantly. ### Relevant Real-Time News Links - [Where Does Tesla Stock Go From Here After the "Mini Disaster?"](https://www.fool.com/investing/2023/10/22/where-does-tesla-stock-go-from-here-after-the-mini/) - [Tesla Investor Says Elon Musk Overplayed Macro Card](https://www.benzinga.com/analyst-ratings/analyst-color/23/10/35365351/tesla-investor-says-elon-musk-overplayed-macro-card-as-he-details-game-plan-to-lift) - [Tesla's Energy Business: Faster-Growing and Now More Profitable](https://www.fool.com/investing/2023/10/22/tsla-energy-business-q3-earnings/) By considering these factors and keeping updated with ongoing developments, investors can make informed decisions on the timing of entry and risk management for TSLA. ``` ## 🌟 Highlights This cookbook has guided you through building a real-time stock research and investment strategy assistant using **LangChain**, **Dappier MCP**, and the `**mcp-use**` Python client. By connecting your agent to live financial tools via MCP, you’ve enabled detailed market analysis and actionable investment recommendations based on up-to-date data. Key components of this workflow include: * **LangChain + OpenAI**: A modular framework for creating LLM-powered applications with agent capabilities. * **Dappier MCP**: A Model Context Protocol server that enables access to real-time, rights-cleared financial tools including stock data, news, and market sentiment. * **mcp-use**: A lightweight open-source client to connect any LLM to any MCP server using `stdio` or `http` transports. This architecture can be extended to build research agents across other financial domains, such as ETF analysis, portfolio monitoring, or earnings prediction using real-time data and tool orchestration.