mcp-server-bigboost MCP Server: Bigboost Data Integration

Overview: What is mcp-server-bigboost MCP Server?

mcp-server-bigboost is an MCP (Model Context Protocol) server designed for seamless integration with the Bigboost API, providing structured data access to personal and corporate information. This server serves as a bridge between AI applications like Claude and real-world datasets managed by Bigdatacorp. By adhering to SOLID and SDR design principles, the server ensures robustness, modularity, and ease of maintenance through well-defined interfaces and standardized methods.

🔧 Core Features & MCP Capabilities

mcp-server-bigboost leverages the Model Context Protocol (MCP) for AI applications to query personal data from Bigboost. The key features include:

• Credibility Assurance: By leveraging Bigboost's API, users can access accurate and up-to-date information about individuals and businesses.
• Comprehensive Data: The server offers detailed profiles of people, including basic details like name, birth date, and contact information, along with business data such as company structure and registration details.
• User-Friendly API: A standard MCP interface for querying datasets ensures consistency across different AI tools.

⚙️ MCP Architecture & Protocol Implementation

The server architecture follows the SOLID and SDR (Structure Decomposition and Reuse) principles. Key components are structured in English using camelCase, such as:

• Configurations (config directory): Configuration files for setting up environment variables.
• Constants (constants directory): Pre-defined values that provide default settings and thresholds.
• Services (services directory): Logic for processing requests from AI applications like Claude.
• Tools (tools directory): Utility functions specific to the MCP protocol, ensuring efficient data handling.
• Types (types directory): Type definitions for better type safety and API documentation.

MCP implements the protocol following these steps:

1. Receive a request in JSON format through stdio (default client use Claude Desktop).
2. Process the request based on predefined service functions.
3. Query Bigboost's API as needed, forwarding structured data back to the AI application.

🚀 Getting Started with Installation

To install and configure mcp-server-bigboost, follow these steps:

1. Install Node.js if you haven't already: A modern JavaScript runtime is essential for running this server.
2. Clone or download the repository: Use Git to clone it from a version control system like GitHub.

   git clone https://github.com/bigdatacorp/mcp-server-bigboost.git
   

3. Install dependencies: Run npm install inside the project directory to set up required packages.
4. Configure environment variables: Set up .env or replace placeholder values in configuration files.

Example of a basic .env file:

BIGBOOST_ACCESS_TOKEN=your-access-token
BIGBOOST_TOKEN_ID=your-token-id

5. Initialize the server: Use npm to start the server.

   node .
   

6. Verify setup: Test the server using tools like Postman or by directly invoking it through a command-line interface.

💡 Key Use Cases in AI Workflows

1. Personal Data Search:

   • Scenario: An assistant receives a query to find out more about a client.
   • Implementation: The user inputs a customer's CPF (Brazilian taxpayer registration number) into the chat, initiating queryPessoa through MCP protocol.

2. Corporate Data Investigation:

   • Scenario: A business analyst needs detailed company structure and ownership details.
   • Implementation: The assistant invokes getCompanyStructure to retrieve comprehensive corporate data.

🔌 Integration with MCP Clients

To fully integrate mcp-server-bigboost with various AI clients:

Integration with Claude Desktop

• Install the server globally:

  npm install -g mcp-server-bigboost
  

• Configure the settings in ~/.codeium/windsurf/mcp_config.json to enable seamless interactions.

Example entry for integration:

{
  "mcpServers": {
    "bigboost-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-server-bigboost"
      ],
      "env": {
        "BIGBOOST_ACCESS_TOKEN": "your-access-token",
        "BIGBOOST_TOKEN_ID": "your-token-id"
      }
    }
  }
}

General Integration Guide

• Start the server using npm start.
• Utilize provided configurations within AI applications like Claude, ensuring they can make effective use of the integrated data sources.

📊 Performance & Compatibility Matrix

mcp-server-bigboost is compatible with a range of MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌ (No Support)	✅	❌ (No Support)	Limited Functionality

🛠️ Advanced Configuration & Security

To optimize the performance and security of mcp-server-bigboost:

• Limitations: Restrict access to specific APIs and tools based on user roles.
• Rate Limiting: Implement rate limiting to prevent abuse. This can be enforced both at the server level and by Bigboost itself.

Example configuration for rate limits in your .env file:

RATE_LIMIT_QUERIES_PER_MINUTE=100

❓ Frequently Asked Questions (FAQ)

Q: Can Bigboost data be integrated with other AI applications?

A: Yes, mcp-server-bigboost supports integration with various MCP clients including Continue and Cursor. For example, the configuration matrix indicates full support for Claude Desktop.

Q: How does mcp-server-bigboost handle sensitive personal information like name and address?

A: The server processes data following strict privacy guidelines to ensure that personally identifiable information remains secure during transit and storage.

Q: Are there special security measures in place for this MCP client compatibility?

A: Yes, we enforce rate limits to prevent abuse and configure stringent access controls. Additionally, encryption is used where necessary to protect sensitive information.

Q: What happens if the Bigboost API service goes down?

A: In case of downtime, mcp-server-bigboost can be configured to use alternative data sources or fallback mechanisms as needed.

Q: How do I update the server's MCP protocol flow for new versions of Bigboost?

A: Regularly check the Bigboost documentation and adjust the integration points within the server code as required.

👨‍💻 Development & Contribution Guidelines

For developers interested in contributing to mcp-server-bigboost:

1. Branching Strategy: Use feature branches to implement changes.
2. Code Testing: Ensure all new features include unit tests through CI/CD pipelines.
3. Documentation Updates: Review and update the documentation as part of any new development.

🌐 MCP Ecosystem & Resources

Explore more about the broader MCP community and related resources:

• MCP Community Website: A hub for developers, integrators, and users to share knowledge and collaborate.
• Bigboost API Documentation: Access detailed guides on using the Bigboost API directly from the official documentation.

Technical Diagrams

Model Context Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Data Architecture Diagram

graph TD
    BigboostAPI[Bigboost API] -->|Data Query| C[MCP Server]
    C --> D[Database Layer]
    D --> E[Application Logic]
    E --> F[Response Processing]
    F --> G[Client Response]
    style BigboostAPI fill:#d7f3e8
    style MCP Server fill:#ffffff
    style Database Layer fill:#d0ebec

Real-World AI Workflow Scenarios

Scenario 1: Personal Data Search

1. User Input: A customer service representative types check CPF ${cpf} in the chat.
2. MCP Protocol Request: The system sends an MCP query to mcp-server-bigboost with the specified CPF.
3. Data Retrieval: Bigboost retrieves detailed personal information corresponding to the CPF.
4. Response Processing: The server processes the data and constructs a structured response.
5. User Feedback: The assistant delivers the results in plain text or rich media format.

Scenario 2: Corporate Data Investigation

1. User Input: An analyst uses investigate company ${company} to get deeper insights.
2. MCP Query: The command is sent via the MCP protocol, triggering mcp-server-bigboost to query Bigboost’s API for comprehensive corporate data.
3. Hierarchical Data Handling: The server handles nested corporate structures, ensuring full transparency in ownership and structure details.
4. Interactive Response: An interactive JSON response with tree-view formatted data allows further analysis by the analyst.

Conclusion

mcp-server-bigboost enhances AI application capabilities by providing efficient access to Bigboost’s extensive datasets through MCP protocol. This comprehensive server setup document aims to facilitate seamless integration, highlight core features, and outline detailed configuration processes for developers looking to leverage its powerful functionalities in their projects.

For any questions or deeper integration details, please refer to the official documentation available in the GitHub repository: GitHub Repository Link.

Note: This is a comprehensive guide designed to help developers understand and fully utilize mcp-server-bigboost within their AI workflows.