Investidor10 - MCP Server: Stock Market Data Integration

Overview: What is Investidor10 - MCP Server?

The Investidor10 - MCP Server project is an integral part of the Model Context Protocol (MCP) ecosystem, designed to provide AI applications with seamless access to stock market data. By integrating specific tools and services from external APIs like Investidor10, this server facilitates the query of real-time stock prices and financial indicators, enabling AI applications such as Claude Desktop, Continue, Cursor, and others to leverage these functionalities through a standardized protocol.

🔧 Core Features & MCP Capabilities

The key features of the Investidor10 - MCP Server include:

• Input Validation: Utilizing Zod for robust data input validation.
• External API Integration: Leveraging fetch infrastructure to interact with the Investidor10 API and retrieve stock market information.
• Tool Registration & Validations: Defining controllers in the src/interface/controllers/Investidor10ToolsController directory that register tools, validate schemas, and return query results.

Real-world AI Workflow Use Cases

Use Case 1: Financial Analysis

An AI assistant can fetch real-time stock prices and financial indicators for a company, then use this data to perform advanced analysis. For example, an application might analyze the historical performance of the S&P 500 index or compare the market trends against specific tech stocks like Apple (AAPL) and Tesla (TSLA). This would be accomplished by querying the Investidor10 API through the MCP server.

graph TD
    A[AI Application] -->|Register Tool| B[MCP Server]
    B --> C[Investidor10 API]
    C --> D[Fetch Data]
    D --> E[Process & Format Data]
    E --> F[MCP Client]
    style A fill:#e1f5fe
    style C fill:#f3e5f5

Use Case 2: Investment Strategy

Investment advisors can use the server to build strategies based on stock market data. By integrating the MCP server, a client like Claude Desktop could request daily stock prices for a list of companies and automatically generate buy/sell recommendations based on specific criteria (e.g., trend analysis).

Real-world Implementation

When an AI application requests stock information from an MCP client, such as Continue Desktop, it communicates with the MCP server via the standardized protocol. The server then interacts with the Investidor10 API to fetch the necessary data and returns it in a structured format that can be easily consumed by the AI application.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the Investidor10 - MCP Server is designed around Domain-Driven Design (DDD) patterns, separating concerns into distinct layers:

Domain Layer

src/domain/
└── models/           # Defines interfaces and types representing data structures such as `Stock`, `Investidor10`.

Infrastructure Layer

src/infrastructure/
└── services/         # Implements external services like `Investidor10ApiService` for making HTTP calls.

Application Layer

src/application/
└── services/         # Contains business logic in `Investidor10Service` for processing and formatting data from the infrastructure layer.

Interface Layer

src/interface/
└── controllers/      # Includes controllers like `Investidor10ToolsController` that register tools, define validation schemas, and return results to the MCP client.

Entry Point

src/main.ts          # Initializes the MCP server, configures the transport (StdioServerTransport), initializes services and controllers, and starts listening on stdio.

🚀 Getting Started with Installation

To set up and run the Investidor10 - MCP Server, follow these steps:

1. Clone the repository from GitHub:

   git clone [email protected]:newerton/mcp-investidor10.git
   

2. Navigate to the project directory:

   cd mcp-investidor10
   

3. Install dependencies using npm:

   npm install
   

4. Build the server:

   npm run build
   

💡 Key Use Cases in AI Workflows

The Investidor10 - MCP Server can be employed to enhance various AI workflows, such as:

• Real-time stock price queries.
• Financial trend analysis and reporting.
• Building investment strategies based on dynamic market data.

By implementing this server, AI applications gain the ability to interact with external tools seamlessly, providing a powerful extension of their core functionalities.

🔌 Integration with MCP Clients

The Investidor10 - MCP Server is compatible with several popular MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

The compatibility matrix and performance of the Investidor10 - MCP Server are outlined as follows:

• Compatibility: The server is designed to be compatible with a wide range of AI clients, including those mentioned in the client compatibility matrix.
• Performance: The implementation optimizes response times through efficient data handling and caching strategies.

🛠️ Advanced Configuration & Security

To configure the Investidor10 - MCP Server for advanced use cases:

Environment Variables

{
  "ENV": "development",
  "API_KEY": "your-api-key-here"
}

You can adjust these settings in src/main.ts to customize behavior and integrate additional security measures.

❓ Frequently Asked Questions (FAQ)

1. How do I validate input data?

   • Input validation is handled using Zod, ensuring that all incoming data conforms to predefined schemas before processing.

2. Can this server work with other APIs besides Investidor10?

   • Absolutely! The infrastructure layer can be extended or replaced with new services to integrate support for different APIs as needed.

3. Does the server require any special security measures?

   • Yes, using environment variables like API_KEY helps secure API calls and protect sensitive information during runtime.

4. How does this help developers building AI applications?

   • By providing a standardized protocol through MCP, this server enables seamless integration of external tools, enhancing the capabilities of AI applications without complex custom implementations.

5. What if I want to contribute or have questions?

   • Pull requests are welcome! Feel free to open issues and discuss improvements on the project repository.

👨‍💻 Development & Contribution Guidelines

To get started contributing to the Investidor10 - MCP Server:

1. Fork the repository from GitHub.
2. Create a new branch for your feature development or bug fixes.
3. Make your changes and ensure all tests pass.
4. Submit a pull request with clear, detailed descriptions of your changes.

🌐 MCP Ecosystem & Resources

The Investidor10 - MCP Server is part of the broader MVP Ecosystem, designed to support developers in building versatile AI applications through a standardized protocol. For more information and resources:

• Visit the official MCP documentation for detailed guides and best practices.
• Explore additional tools and services compatible with MCP clients at the MCP Marketplace.

By contributing to this project, you help expand the capabilities of AI development and empower the broader developer community.

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This comprehensive documentation highlights the core features, architecture, installation process, and use cases for the Investidor10 - MCP Server. It also includes an advanced configuration section to assist developers in setting it up for their specific needs.